import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import warnings
import json
import glob
from pathlib import Path
import plotly.express as px
import plotly.graph_objects as go
from ipl_functions import formula_barplot, formula_scatterplot, formula_scatterplot_multiple_data
from ipl_functions import all_players_stats_year, all_players_stats_year_list, season_player_team_stats
from ipl_functions import plot_all_top_5_dict, plot_team_top_5
pd.set_option('display.max_columns', 100)
plt.style.use('dark_background')
warnings.filterwarnings('ignore')
all_data = []
for p in glob.glob('../Data/ipl_json/*.json'):
with open(p) as file:
data = json.load(file)
all_data.append(data)
all_teams = []
for i in all_data:
for j in i['info']['teams']:
if j not in all_teams:
all_teams.append(j)
#all_teams
all_players = []
for i in all_teams:
for j in all_data:
try:
if j['info']['players'][i]:
for k in j['info']['players'][i]:
if k not in all_players:
all_players.append(k)
except:
None
all_cols = ['player_name', 'matches_played', 'innings_batted', 'innings_bowled', 'matches_won', 'runs_scored',
'balls_played', 'batting_avg', 'batting_strike_rate', 'catches', 'run_outs', 'balls_bowled', 'runs_given',
'wickets_taken', 'wickets_per_innings', 'bowling_avg', 'bowling_strike_rate', 'bowling_eco',
'four_scored', 'six_scored', 'four_given', 'six_given', 'extras_for', 'extras_against', 'scores_above_30',
'strike_rate_30', 'multi_wickets', 'man_of_match', 'trophies', 'formula_batter', 'formula_bowler', 'formula_fielder']
all_players_year_df = pd.DataFrame(columns=all_cols)
player_08 = all_players_stats_year(all_players, all_data, all_players_year_df, '2008')
all_players_year_df = pd.DataFrame(columns=all_cols)
player_09 = all_players_stats_year(all_players, all_data, all_players_year_df, '2009')
all_players_year_df = pd.DataFrame(columns=all_cols)
player_10 = all_players_stats_year(all_players, all_data, all_players_year_df, '2010')
all_players_year_df = pd.DataFrame(columns=all_cols)
player_11 = all_players_stats_year(all_players, all_data, all_players_year_df, '2011')
all_players_year_df = pd.DataFrame(columns=all_cols)
player_12 = all_players_stats_year(all_players, all_data, all_players_year_df, '2012')
all_players_year_df = pd.DataFrame(columns=all_cols)
player_13 = all_players_stats_year(all_players, all_data, all_players_year_df, '2013')
all_players_year_df = pd.DataFrame(columns=all_cols)
player_14 = all_players_stats_year(all_players, all_data, all_players_year_df, '2014')
all_players_year_df = pd.DataFrame(columns=all_cols)
player_15 = all_players_stats_year(all_players, all_data, all_players_year_df, '2015')
all_players_year_df = pd.DataFrame(columns=all_cols)
player_16 = all_players_stats_year(all_players, all_data, all_players_year_df, '2016')
all_players_year_df = pd.DataFrame(columns=all_cols)
player_17 = all_players_stats_year(all_players, all_data, all_players_year_df, '2017')
all_players_year_df = pd.DataFrame(columns=all_cols)
player_18 = all_players_stats_year(all_players, all_data, all_players_year_df, '2018')
all_players_year_df = pd.DataFrame(columns=all_cols)
player_19 = all_players_stats_year(all_players, all_data, all_players_year_df, '2019')
all_players_year_df = pd.DataFrame(columns=all_cols)
player_20 = all_players_stats_year(all_players, all_data, all_players_year_df, '2020')
all_players_year_df = pd.DataFrame(columns=all_cols)
player_21 = all_players_stats_year(all_players, all_data, all_players_year_df, '2021')
all_players_year_df = pd.DataFrame(columns=all_cols)
player_22 = all_players_stats_year(all_players, all_data, all_players_year_df, '2022')
all_players_year_df = pd.DataFrame(columns=all_cols)
player_23 = all_players_stats_year(all_players, all_data, all_players_year_df, '2023')
all_players_year_df = pd.DataFrame(columns=all_cols)
player_24 = all_players_stats_year(all_players, all_data, all_players_year_df, '2024')
player_team = {}
for i in all_players:
all_teams_played = []
for j in all_teams:
xy = {}
yy = []
for k in all_data:
for t in k['info']['players'].keys():
if t == j and i in k['info']['players'][t]:
if k['info']['dates'][0][:4] not in yy:
yy.append(k['info']['dates'][0][:4])
xy[t] = yy
if xy:
all_teams_played.append(xy)
#all_teams_played.append(j)
player_team[str(i)] = all_teams_played
import json
with open('player_team_year', 'w') as file:
json.dump(player_team, file)
player_team['R Dravid']
[{'Royal Challengers Bangalore': ['2008', '2009', '2010']},
{'Rajasthan Royals': ['2011', '2012', '2013']}]
#These data frames were parsed in ipl.ipynb
player_df_2010_14 = pd.read_csv('../Data/ipl_all_player_df_2010_14.csv')
player_df_2015_19 = pd.read_csv('../Data/ipl_all_player_df_2015_19.csv')
player_df_2020_24 = pd.read_csv('../Data/ipl_all_player_df_2020_24.csv')
all_players_df = pd.read_csv('../Data/ipl_all_player_df.csv')
formula_scatterplot_multiple_data(player_df_2010_14, player_df_2015_19, player_df_2020_24,
'matches_played', 'runs_scored', 'four_scored', 'matches_played',
['player_name', 'batting_strike_rate', 'matches_won'], '2010-2014', '2015-2019', '2020-2024')
formula_scatterplot_multiple_data(player_df_2010_14, player_df_2015_19, player_df_2020_24,
'runs_scored' , 'runs_scored', 'six_scored', 'matches_played',
['player_name', 'batting_strike_rate', 'matches_won'],'2010-2014', '2015_-2019', '2020-2024')
formula_scatterplot_multiple_data(player_df_2010_14, player_df_2015_19, player_df_2020_24,
'matches_played', 'runs_scored', 'scores_above_30', 'matches_played',
['player_name', 'strike_rate_30', 'matches_won'],'2010-2014', '2015_-2019', '2020-2024')
formula_scatterplot_multiple_data(player_df_2010_14, player_df_2015_19, player_df_2020_24,
'runs_scored', 'matches_played', 'strike_rate_30', 'matches_played',
['player_name', 'batting_strike_rate', 'matches_won'], '2010-2014', '2015_-2019', '2020-2024')
formula_scatterplot_multiple_data(player_df_2010_14, player_df_2015_19, player_df_2020_24,
'wickets_taken', 'matches_played', 'multi_wickets', 'matches_played',
['player_name', 'bowling_eco'], '2010-2014', '2015_-2019', '2020-2024')
formula_scatterplot_multiple_data(player_df_2010_14, player_df_2015_19, player_df_2020_24,
'wickets_taken', 'matches_played', 'four_given', 'innings_bowled',
['player_name', 'bowling_eco'], '2010-2014', '2015_-2019', '2020-2024')
formula_scatterplot_multiple_data(player_df_2010_14, player_df_2015_19, player_df_2020_24,
'wickets_taken', 'matches_played', 'six_given', 'innings_bowled',
['player_name', 'bowling_eco'], '2010-2014', '2015_-2019', '2020-2024')
formula_scatterplot_multiple_data(player_df_2010_14, player_df_2015_19, player_df_2020_24,
'formula_fielder', 'matches_played', 'catches', 'matches_played',
['player_name', 'runs_scored', 'wickets_taken'], '2010-2014', '2015_-2019', '2020-2024')
formula_scatterplot_multiple_data(player_df_2010_14, player_df_2015_19, player_df_2020_24,
'formula_fielder', 'matches_played', 'run_outs', 'matches_played',
'player_name', '2010-2014', '2015_-2019', '2020-2024')
formula_scatterplot_multiple_data(player_df_2010_14, player_df_2015_19, player_df_2020_24,
'man_of_match', 'matches_played', 'man_of_match', 'matches_played',
['player_name', 'runs_scored', 'scores_above_30', 'batting_strike_rate'], '2010-2014', '2015_-2019', '2020-2024')
years = ['2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015',
'2016', '2017', '2018', '2019', '2020', '2021', '2022', '2023',
'2024']
player_years_data = [player_08, player_09, player_10, player_11, player_12,
player_13, player_14, player_15, player_16, player_17,
player_18, player_19, player_20, player_21, player_22,
player_23, player_24]
player_runs_dict = {}
player_sr_dict = {}
player_runs_30_dict = {}
player_sr_30_dict = {}
player_four_scored_dict = {}
player_six_scored_dict = {}
player_wickets_dict = {}
player_multi_wickets_dict = {}
player_bowling_eco_dict = {}
player_bowling_sr_dict = {}
player_four_given_dict = {}
player_six_given_dict = {}
player_catches_dict = {}
player_runouts_dict = {}
player_matches_dict = {}
for i in all_players:
runs = []
sr = []
runs_30 = []
sr_30 = []
four_for = []
six_for = []
wickets = []
multi_wickets = []
bowling_eco = []
bowling_sr = []
four_against = []
six_against = []
catches = []
run_outs = []
matches_played = []
for data in player_years_data:
if i in data['player_name'].tolist():
runs.append(data[data['player_name'] == i]['runs_scored'].values[0])
matches_played.append(data[data['player_name'] == i]['matches_played'].values[0])
sr.append(np.round(data[data['player_name'] == i]['batting_strike_rate'].values[0], 2))
runs_30.append(data[data['player_name'] == i]['scores_above_30'].values[0])
sr_30.append(np.round(data[data['player_name'] == i]['strike_rate_30'].values[0], 2))
four_for.append(data[data['player_name'] == i]['four_scored'].values[0])
six_for.append(data[data['player_name'] == i]['six_scored'].values[0])
wickets.append(data[data['player_name'] == i]['wickets_taken'].values[0])
multi_wickets.append(data[data['player_name'] == i]['multi_wickets'].values[0])
bowling_eco.append(np.round(data[data['player_name'] == i]['bowling_eco'].values[0], 2))
bowling_sr.append(np.round(data[data['player_name'] == i]['bowling_strike_rate'].values[0], 2))
four_against.append(data[data['player_name'] == i]['four_given'].values[0])
six_against.append(data[data['player_name'] == i]['six_given'].values[0])
catches.append(data[data['player_name'] == i]['catches'].values[0])
run_outs.append(data[data['player_name'] == i]['run_outs'].values[0])
player_runs_dict[i] = runs
player_matches_dict[i] = matches_played
player_sr_dict[i] = sr
player_runs_30_dict[i] = runs_30
player_sr_30_dict[i] = sr_30
player_four_scored_dict[i] = four_for
player_six_scored_dict[i] = six_for
player_wickets_dict[i] = wickets
player_multi_wickets_dict[i] = multi_wickets
player_bowling_eco_dict[i] = bowling_eco
player_bowling_sr_dict[i] = bowling_sr
player_four_given_dict[i] = four_against
player_six_given_dict[i] = six_against
player_catches_dict[i] = catches
player_runouts_dict[i] = run_outs
winning_years_team = {}
for player in all_players:
winner = []
for i in all_teams:
for game in all_data:
try:
if player in game['info']['players'][i]:
try:
if game['info']['event']['stage'] == 'Final' and game['info']['outcome']['winner'] == i:
winner.append(game['info']['dates'][0][:4])
except:
None
except:
None
winning_years_team[player] = winner
team_player_stats_2008 = season_player_team_stats(all_data, all_teams, all_players, all_teams, year='2008')
team_player_stats_2009 = season_player_team_stats(all_data, all_teams, all_players, all_teams, year='2009')
team_player_stats_2010 = season_player_team_stats(all_data, all_teams, all_players, all_teams, year='2010')
team_player_stats_2011 = season_player_team_stats(all_data, all_teams, all_players, all_teams, year='2011')
team_player_stats_2012 = season_player_team_stats(all_data, all_teams, all_players, all_teams, year='2012')
team_player_stats_2013 = season_player_team_stats(all_data, all_teams, all_players, all_teams, year='2013')
team_player_stats_2014 = season_player_team_stats(all_data, all_teams, all_players, all_teams, year='2014')
team_player_stats_2015 = season_player_team_stats(all_data, all_teams, all_players, all_teams, year='2015')
team_player_stats_2016 = season_player_team_stats(all_data, all_teams, all_players, all_teams, year='2016')
team_player_stats_2017 = season_player_team_stats(all_data, all_teams, all_players, all_teams, year='2017')
team_player_stats_2018 = season_player_team_stats(all_data, all_teams, all_players, all_teams, year='2018')
team_player_stats_2019 = season_player_team_stats(all_data, all_teams, all_players, all_teams, year='2019')
team_player_stats_2020 = season_player_team_stats(all_data, all_teams, all_players, all_teams, year='2020')
team_player_stats_2021 = season_player_team_stats(all_data, all_teams, all_players, all_teams, year='2021')
team_player_stats_2022 = season_player_team_stats(all_data, all_teams, all_players, all_teams, year='2022')
team_player_stats_2023 = season_player_team_stats(all_data, all_teams, all_players, all_teams, year='2023')
team_player_stats_2024 = season_player_team_stats(all_data, all_teams, all_players, all_teams, year='2024')
all_team_player_stats = [team_player_stats_2008, team_player_stats_2009, team_player_stats_2010,
team_player_stats_2011, team_player_stats_2012, team_player_stats_2013,
team_player_stats_2014, team_player_stats_2015, team_player_stats_2016,
team_player_stats_2017, team_player_stats_2018, team_player_stats_2019,
team_player_stats_2020, team_player_stats_2021, team_player_stats_2022,
team_player_stats_2023, team_player_stats_2024]
def top_5_performers_all_team(team_player_stats, sortby='total_runs'):
'''
A function which takes a dictionary of team_player stats,
team name and returns the best players in the team for the season,
most runs, most wickets, most catches, dot_balls, most 4s, 6s.
'''
return_dict = {}
for team in team_player_stats.keys():
return_dict[team] = [k for k, v in sorted(team_player_stats[team].items(), key=lambda item: item[1][sortby], reverse=True)][:5]
return return_dict
top_5_runs_scored_2008 = top_5_performers_all_team(team_player_stats_2008)
top_5_wickets_2008 = top_5_performers_all_team(team_player_stats_2008, sortby='total_wickets')
top_5_catches_2008 = top_5_performers_all_team(team_player_stats_2008, sortby='total_catches')
top_5_runs_given_2008 = top_5_performers_all_team(team_player_stats_2008, sortby='total_runs_given')
top_5_dot_balls_2008 = top_5_performers_all_team(team_player_stats_2008, sortby='total_dot_balls_bowled')
top_5_four_for_2008 = top_5_performers_all_team(team_player_stats_2008, sortby='total_four_for')
top_5_six_for_2008 = top_5_performers_all_team(team_player_stats_2008, sortby='total_six_for')
plot_team_top_5(top_5_runs_scored_2008, team_player_stats_2008,'Mumbai Indians', yval='runs')
plot_team_top_5(top_5_wickets_2008, team_player_stats_2008, 'Mumbai Indians', yval='wickets')
plot_team_top_5(top_5_runs_scored_2008, team_player_stats_2008,'Chennai Super Kings', yval='runs')
plot_team_top_5(top_5_wickets_2008, team_player_stats_2008, 'Chennai Super Kings', yval='wickets')
plot_team_top_5(top_5_runs_scored_2008, team_player_stats_2008,'Kolkata Knight Riders', yval='runs')
plot_team_top_5(top_5_wickets_2008, team_player_stats_2008, 'Kolkata Knight Riders', yval='wickets')
plot_team_top_5(top_5_runs_scored_2008, team_player_stats_2008,'Rajasthan Royals', yval='runs')
plot_team_top_5(top_5_wickets_2008, team_player_stats_2008, 'Rajasthan Royals', yval='wickets')
plot_team_top_5(top_5_runs_scored_2008, team_player_stats_2008,'Delhi Daredevils', yval='runs')
plot_team_top_5(top_5_wickets_2008, team_player_stats_2008, 'Delhi Daredevils', yval='wickets')
plot_team_top_5(top_5_runs_scored_2008, team_player_stats_2008,'Kings XI Punjab', yval='runs')
plot_team_top_5(top_5_wickets_2008, team_player_stats_2008, 'Kings XI Punjab', yval='wickets')
plot_team_top_5(top_5_runs_scored_2008, team_player_stats_2008,'Deccan Chargers', yval='runs')
plot_team_top_5(top_5_wickets_2008, team_player_stats_2008, 'Deccan Chargers', yval='wickets')
plot_team_top_5(top_5_runs_scored_2008, team_player_stats_2008,'Royal Challengers Bangalore', yval='runs')
plot_team_top_5(top_5_wickets_2008, team_player_stats_2008, 'Royal Challengers Bangalore', yval='wickets')
top_5_runs_scored_2016 = top_5_performers_all_team(team_player_stats_2016)
top_5_wickets_2016 = top_5_performers_all_team(team_player_stats_2016, sortby='total_wickets')
top_5_catches_20016 = top_5_performers_all_team(team_player_stats_2016, sortby='total_catches')
top_5_runs_given_2016 = top_5_performers_all_team(team_player_stats_2016, sortby='total_runs_given')
top_5_dot_balls_2016 = top_5_performers_all_team(team_player_stats_2016, sortby='total_dot_balls_bowled')
top_5_four_for_2016 = top_5_performers_all_team(team_player_stats_2016, sortby='total_four_for')
top_5_six_for_2016 = top_5_performers_all_team(team_player_stats_2016, sortby='total_six_for')
player_16.head()
| player_name | matches_played | innings_batted | innings_bowled | matches_won | runs_scored | balls_played | batting_avg | batting_strike_rate | catches | run_outs | balls_bowled | runs_given | wickets_taken | wickets_per_innings | bowling_avg | bowling_strike_rate | bowling_eco | four_scored | six_scored | four_given | six_given | extras_for | extras_against | scores_above_30 | strike_rate_30 | multi_wickets | man_of_match | trophies | formula_batter | formula_bowler | formula_fielder | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | DA Warner | 17 | 17 | 0 | 11 | 848 | 579 | 49.882353 | 146.459413 | 4 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 88 | 31 | 0 | 0 | 36 | 0 | 16 | 157.743503 | 0 | 3 | 1 | 2692.778403 | 0.0 | 7 |
| 1 | S Dhawan | 17 | 17 | 0 | 11 | 501 | 438 | 29.470588 | 114.383562 | 5 | 1 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 51 | 8 | 0 | 0 | 22 | 0 | 12 | 118.780377 | 0 | 0 | 1 | 1513.835111 | 0.0 | 6 |
| 2 | MC Henriques | 17 | 15 | 17 | 11 | 182 | 162 | 12.133333 | 112.345679 | 11 | 3 | 318 | 409 | 12 | 0.705882 | 34.083333 | 26.5 | 7.716981 | 15 | 6 | 29 | 16 | 7 | 9 | 4 | 184.52381 | 3 | 1 | 1 | 771.228571 | 15.705882 | 15 |
| 3 | Yuvraj Singh | 10 | 10 | 5 | 7 | 236 | 183 | 23.6 | 128.961749 | 1 | 1 | 38 | 51 | 0 | 0.0 | 0.0 | 0.0 | 8.052632 | 22 | 13 | 2 | 2 | 6 | 1 | 7 | 156.219662 | 0 | 0 | 1 | 1152.137634 | 0.0 | 2 |
| 4 | DJ Hooda | 17 | 15 | 6 | 11 | 144 | 125 | 9.6 | 115.2 | 3 | 2 | 80 | 94 | 3 | 0.5 | 31.333333 | 26.666667 | 7.05 | 9 | 5 | 6 | 3 | 7 | 3 | 1 | 154.545455 | 1 | 0 | 1 | 178.145455 | 4.5 | 5 |
plot_team_top_5(top_5_runs_scored_2016, team_player_stats_2016,'Mumbai Indians', yval='runs')
plot_team_top_5(top_5_wickets_2016, team_player_stats_2016, 'Mumbai Indians', yval='wickets')
plot_team_top_5(top_5_runs_scored_2016, team_player_stats_2016,'Chennai Super Kings', yval='runs')
plot_team_top_5(top_5_wickets_2016, team_player_stats_2016, 'Chennai Super Kings', yval='wickets')
plot_team_top_5(top_5_runs_scored_2016, team_player_stats_2016,'Kolkata Knight Riders', yval='runs')
plot_team_top_5(top_5_wickets_2016, team_player_stats_2016, 'Kolkata Knight Riders', yval='wickets')
plot_team_top_5(top_5_runs_scored_2016, team_player_stats_2016,'Rajasthan Royals', yval='runs')
plot_team_top_5(top_5_wickets_2016, team_player_stats_2016, 'Rajasthan Royals', yval='wickets')
plot_team_top_5(top_5_runs_scored_2016, team_player_stats_2016,'Royal Challengers Bangalore', yval='runs')
plot_team_top_5(top_5_wickets_2016, team_player_stats_2016, 'Royal Challengers Bangalore', yval='wickets')
plot_team_top_5(top_5_runs_scored_2016, team_player_stats_2016,'Sunrisers Hyderabad', yval='runs')
plot_team_top_5(top_5_wickets_2016, team_player_stats_2016, 'Sunrisers Hyderabad', yval='wickets')
plot_team_top_5(top_5_runs_scored_2016, team_player_stats_2016,'Delhi Daredevils', yval='runs')
plot_team_top_5(top_5_wickets_2016, team_player_stats_2016, 'Delhi Daredevils', yval='wickets')
plot_team_top_5(top_5_runs_scored_2016, team_player_stats_2016,'Kings XI Punjab', yval='runs')
plot_team_top_5(top_5_wickets_2016, team_player_stats_2016, 'Kings XI Punjab', yval='wickets')
top_5_runs_scored_2024 = top_5_performers_all_team(team_player_stats_2024)
top_5_wickets_2024 = top_5_performers_all_team(team_player_stats_2024, sortby='total_wickets')
top_5_catches_20024 = top_5_performers_all_team(team_player_stats_2024, sortby='total_catches')
top_5_runs_given_2024 = top_5_performers_all_team(team_player_stats_2024, sortby='total_runs_given')
top_5_dot_balls_2024 = top_5_performers_all_team(team_player_stats_2024, sortby='total_dot_balls_bowled')
top_5_four_for_2024 = top_5_performers_all_team(team_player_stats_2024, sortby='total_four_for')
top_5_six_for_2024 = top_5_performers_all_team(team_player_stats_2024, sortby='total_six_for')
player_24.head()
| player_name | matches_played | innings_batted | innings_bowled | matches_won | runs_scored | balls_played | batting_avg | batting_strike_rate | catches | run_outs | balls_bowled | runs_given | wickets_taken | wickets_per_innings | bowling_avg | bowling_strike_rate | bowling_eco | four_scored | six_scored | four_given | six_given | extras_for | extras_against | scores_above_30 | strike_rate_30 | multi_wickets | man_of_match | trophies | formula_batter | formula_bowler | formula_fielder | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | DA Warner | 8 | 8 | 0 | 2 | 168 | 130 | 21.0 | 129.230769 | 5 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 17 | 10 | 0 | 0 | 13 | 0 | 3 | 147.086835 | 0 | 0 | 0 | 489.260504 | 0.0 | 5 |
| 1 | S Dhawan | 5 | 5 | 0 | 2 | 152 | 125 | 30.4 | 121.6 | 3 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 18 | 4 | 0 | 0 | 6 | 0 | 3 | 125.614035 | 0 | 0 | 0 | 429.242105 | 0.0 | 3 |
| 2 | MC Henriques | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NaN | 0 | 0 | 0 | NaN | 0.0 | 0 |
| 3 | Yuvraj Singh | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NaN | 0 | 0 | 0 | NaN | 0.0 | 0 |
| 4 | DJ Hooda | 11 | 9 | 2 | 4 | 145 | 107 | 16.111111 | 135.514019 | 4 | 0 | 19 | 21 | 0 | 0.0 | 0.0 | 0.0 | 6.631579 | 16 | 3 | 2 | 0 | 6 | 1 | 2 | 156.25 | 0 | 0 | 0 | 347.611111 | 0.0 | 4 |
plot_team_top_5(top_5_runs_scored_2024, team_player_stats_2024,'Kolkata Knight Riders', yval='runs')
plot_team_top_5(top_5_wickets_2024, team_player_stats_2024, 'Kolkata Knight Riders', yval='wickets')
plot_team_top_5(top_5_runs_scored_2024, team_player_stats_2024,'Chennai Super Kings', yval='runs')
plot_team_top_5(top_5_wickets_2024, team_player_stats_2024, 'Chennai Super Kings', yval='wickets')
plot_team_top_5(top_5_runs_scored_2024, team_player_stats_2024,'Mumbai Indians', yval='runs')
plot_team_top_5(top_5_wickets_2024, team_player_stats_2024, 'Mumbai Indians', yval='wickets')
plot_team_top_5(top_5_runs_scored_2024, team_player_stats_2024,'Rajasthan Royals', yval='runs')
plot_team_top_5(top_5_wickets_2024, team_player_stats_2024, 'Rajasthan Royals', yval='wickets')
plot_team_top_5(top_5_runs_scored_2024, team_player_stats_2024,'Royal Challengers Bengaluru', yval='runs')
plot_team_top_5(top_5_wickets_2024, team_player_stats_2024, 'Royal Challengers Bengaluru', yval='wickets')
plot_team_top_5(top_5_runs_scored_2024, team_player_stats_2024,'Sunrisers Hyderabad', yval='runs')
plot_team_top_5(top_5_wickets_2024, team_player_stats_2024, 'Sunrisers Hyderabad', yval='wickets')
plot_team_top_5(top_5_runs_scored_2024, team_player_stats_2024,'Delhi Capitals', yval='runs')
plot_team_top_5(top_5_wickets_2024, team_player_stats_2024, 'Delhi Capitals', yval='wickets')
plot_team_top_5(top_5_runs_scored_2024, team_player_stats_2024, 'Punjab Kings', yval='runs')
plot_team_top_5(top_5_wickets_2024, team_player_stats_2024, 'Punjab Kings', yval='wickets')
#player_df['formula_batter'] = ((player_df['four_scored'] + player_df['six_scored'] + player_df['batting_avg']) + (player_df['scores_above_30'] * player_df['strike_rate_30']))
#player_df['formula_bowler'] = ((player_df['wickets_taken'] + player_df['multi_wickets']) + (player_df['wickets_taken']/player_df['matches_played']))
#player_df['formula_fielder'] = (player_df['man_of_match'] + player_df['catches'] + player_df['run_outs'])
top_20_2010_14_bat = player_df_2010_14.sort_values('formula_batter', ascending=False).head(20)['player_name']
top_20_2015_19_bat = player_df_2015_19.sort_values('formula_batter', ascending=False).head(20)['player_name']
top_20_2020_24_bat = player_df_2020_24.sort_values('formula_batter', ascending=False).head(20)['player_name']
top_20_2010_14_bowl = player_df_2010_14.sort_values('formula_bowler', ascending=False).head(20)['player_name']
top_20_2015_19_bowl = player_df_2015_19.sort_values('formula_bowler', ascending=False).head(20)['player_name']
top_20_2020_24_bowl = player_df_2020_24.sort_values('formula_bowler', ascending=False).head(20)['player_name']
top_20_2010_14_field = player_df_2010_14.sort_values('formula_fielder', ascending=False).head(20)['player_name']
top_20_2015_19_field = player_df_2015_19.sort_values('formula_fielder', ascending=False).head(20)['player_name']
top_20_2020_24_field = player_df_2020_24.sort_values('formula_fielder', ascending=False).head(20)['player_name']
for player in top_20_2010_14_bat[:10]:
#print(type(player))
try:
fig = px.scatter(player_runs_dict, y=str(player), x=years, hover_data=[player_sr_dict[player], player_matches_dict[player]])
fig.add_trace(go.Scatter(x=winning_years_team[str(player)],
mode='markers', name='winning_year'))
#fig.update_traces(hovertext=player_sr_dict[player])
fig.update_layout(xaxis_title='years', title='2010-2014')
fig.show()
except Exception as e:
print(str(e))
for player in top_20_2015_19_bat[:10]:
#print(type(player))
try:
fig = px.scatter(player_runs_dict, y=str(player), x=years, hover_data=[player_sr_dict[player], player_matches_dict[player]])
fig.add_trace(go.Scatter(x=winning_years_team[str(player)],
mode='markers', name='winning_year'))
#fig.update_traces(hovertext=player_sr_dict[player])
fig.update_layout(xaxis_title='years', title='2015-2019')
fig.show()
except Exception as e:
print(str(e))
for player in top_20_2020_24_bat[:10]:
#print(type(player))
try:
fig = px.scatter(player_runs_dict, y=str(player), x=years, hover_data=[player_sr_dict[player], player_matches_dict[player]])
fig.add_trace(go.Scatter(x=winning_years_team[str(player)],
mode='markers', name='winning_year'))
#fig.update_traces(hovertext=player_sr_dict[player])
fig.update_layout(xaxis_title='years', title='2020-2024')
fig.show()
except Exception as e:
print(str(e))
for player in top_20_2010_14_bowl[:10]:
#print(type(player))
try:
fig = px.scatter(player_wickets_dict, y=str(player), x=years, hover_data=[player_bowling_eco_dict[player], player_matches_dict[player]])
fig.add_trace(go.Scatter(x=winning_years_team[str(player)],
mode='markers', name='winning_year'))
#fig.update_traces(hovertext=player_sr_dict[player])
fig.update_layout(xaxis_title='years', title='2010-2014')
fig.show()
except Exception as e:
print(str(e))
for player in top_20_2015_19_bowl[:10]:
#print(type(player))
try:
fig = px.scatter(player_wickets_dict, y=str(player), x=years, hover_data=[player_bowling_eco_dict[player], player_matches_dict[player]])
fig.add_trace(go.Scatter(x=winning_years_team[str(player)],
mode='markers', name='winning_year'))
#fig.update_traces(hovertext=player_sr_dict[player])
fig.update_layout(xaxis_title='years', title='2015-2019')
fig.show()
except Exception as e:
print(str(e))
for player in top_20_2020_24_bowl[:10]:
#print(type(player))
try:
fig = px.scatter(player_wickets_dict, y=str(player), x=years, hover_data=[player_bowling_eco_dict[player], player_matches_dict[player]])
fig.add_trace(go.Scatter(x=winning_years_team[str(player)],
mode='markers', name='winning_year'))
#fig.update_traces(hovertext=player_sr_dict[player])
fig.update_layout(xaxis_title='years', title='2020-2024')
fig.show()
except Exception as e:
print(str(e))
for player in top_20_2010_14_field[:10]:
#print(type(player))
try:
fig = px.scatter(player_catches_dict, y=str(player), x=years, hover_data=[player_runouts_dict[player], player_matches_dict[player]])
fig.add_trace(go.Scatter(x=winning_years_team[str(player)],
mode='markers', name='winning_year'))
#fig.update_traces(hovertext=player_sr_dict[player])
fig.update_layout(xaxis_title='years', title='2010-2014')
fig.show()
except Exception as e:
print(str(e))
for player in top_20_2015_19_field[:10]:
#print(type(player))
try:
fig = px.scatter(player_catches_dict, y=str(player), x=years, hover_data=[player_runouts_dict[player], player_matches_dict[player]])
fig.add_trace(go.Scatter(x=winning_years_team[str(player)],
mode='markers', name='winning_year'))
#fig.update_traces(hovertext=player_sr_dict[player])
fig.update_layout(xaxis_title='years', title='2015-2019')
fig.show()
except Exception as e:
print(str(e))
for player in top_20_2020_24_field[:10]:
#print(type(player))
try:
fig = px.scatter(player_catches_dict, y=str(player), x=years, hover_data=[player_runouts_dict[player], player_matches_dict[player]])
fig.add_trace(go.Scatter(x=winning_years_team[str(player)],
mode='markers', name='winning_year'))
#fig.update_traces(hovertext=player_sr_dict[player])
fig.update_layout(xaxis_title='years', title='2020-2024')
fig.show()
except Exception as e:
print(str(e))
all_mi = []
all_csk = []
all_hyd = []
all_rcb = []
all_pk = []
all_rr = []
all_dd = []
all_kkr = []
all_gt = []
all_lsg = []
for i in all_data:
for j in i['info']['teams']:
if j == 'Mumbai Indians':
all_mi.append(i)
for i in all_data:
for j in i['info']['teams']:
if j in ['Sunrisers Hyderabad', 'Deccan Chargers']:
all_hyd.append(i)
for i in all_data:
for j in i['info']['teams']:
if j == 'Royal Challengers Bangalore':
all_rcb.append(i)
for i in all_data:
for j in i['info']['teams']:
if j == 'Chennai Super Kings':
all_csk.append(i)
for i in all_data:
for j in i['info']['teams']:
if j in ['Punjab Kings', 'Kings XI Punjab']:
all_pk.append(i)
for i in all_data:
for j in i['info']['teams']:
if j == 'Rajasthan Royals':
all_rr.append(i)
for i in all_data:
for j in i['info']['teams']:
if j in ['Delhi Daredevils', 'Delhi Capitals']:
all_dd.append(i)
for i in all_data:
for j in i['info']['teams']:
if j == 'Kolkata Knight Riders':
all_kkr.append(i)
for i in all_data:
for j in i['info']['teams']:
if j == 'Gujarat Titans':
all_gt.append(i)
for i in all_data:
for j in i['info']['teams']:
if j == 'Lucknow Super Giants':
all_lsg.append(i)
winning_years = {}
losing_years = {}
for team in all_teams:
winners = []
losers = []
for data in all_data:
try:
if data['info']['event']['stage'] == 'Final' and data['info']['outcome']['winner'] == team:
winners.append(data['info']['dates'][0][:4])
except:
None
try:
if data['info']['event']['stage'] == 'Final' and data['info']['outcome']['winner'] != team:
losers.append(data['info']['dates'][0][:4])
except:
None
winning_years[team] = winners
losing_years[team] = losers
winning_years
{'Sunrisers Hyderabad': ['2016'],
'Royal Challengers Bangalore': [],
'Rising Pune Supergiant': [],
'Mumbai Indians': ['2017', '2019', '2020', '2013', '2015'],
'Gujarat Lions': [],
'Kolkata Knight Riders': ['2024', '2012', '2014'],
'Kings XI Punjab': [],
'Delhi Daredevils': [],
'Chennai Super Kings': ['2018', '2021', '2023', '2010', '2011'],
'Rajasthan Royals': ['2008'],
'Delhi Capitals': [],
'Punjab Kings': [],
'Lucknow Super Giants': [],
'Gujarat Titans': ['2022'],
'Royal Challengers Bengaluru': [],
'Deccan Chargers': ['2009'],
'Kochi Tuskers Kerala': [],
'Pune Warriors': [],
'Rising Pune Supergiants': []}
winning_years['Sunrisers Hyderabad'].append('2009')
all_cols = ['player_name', 'matches_played', 'innings_batted', 'innings_bowled', 'matches_won', 'runs_scored',
'balls_played', 'batting_avg', 'batting_strike_rate', 'catches', 'run_outs', 'balls_bowled', 'runs_given',
'wickets_taken', 'wickets_per_innings', 'bowling_avg', 'bowling_strike_rate', 'bowling_eco',
'four_scored', 'six_scored', 'four_given', 'six_given', 'extras_for', 'extras_against', 'scores_above_30',
'strike_rate_30', 'multi_wickets', 'man_of_match', 'trophies', 'formula_batter', 'formula_bowler', 'formula_fielder']
all_players_year_df = pd.DataFrame(columns=all_cols)
mi_winning_player_stats = all_players_stats_year_list(all_players, all_data, all_players_year_df, winning_years['Mumbai Indians'])
#mi_winning_player_stats = mi_winning_player_stats[mi_winning_player_stats['matches_played'] != 0 ]
all_players_year_df = pd.DataFrame(columns=all_cols)
csk_winning_player_stats = all_players_stats_year_list(all_players, all_data, all_players_year_df, winning_years['Chennai Super Kings'])
#csk_winning_player_stats = csk_winning_player_stats[csk_winning_player_stats['matches_played'] != 0 ]
all_players_year_df = pd.DataFrame(columns=all_cols)
kkr_winning_player_stats = all_players_stats_year_list(all_players, all_data, all_players_year_df, winning_years['Kolkata Knight Riders'])
#kkr_winning_player_stats = kkr_winning_player_stats[kkr_winning_player_stats['matches_played'] != 0 ]
all_players_year_df = pd.DataFrame(columns=all_cols)
rr_winning_player_stats = all_players_stats_year_list(all_players, all_data, all_players_year_df, winning_years['Rajasthan Royals'])
#srh_winning_player_stats = srh_winning_player_stats[srh_winning_player_stats['matches_played'] != 0 ]
all_players_year_df = pd.DataFrame(columns=all_cols)
gt_winning_player_stats = all_players_stats_year_list(all_players, all_data, all_players_year_df, winning_years['Gujarat Titans'])
#gt_winning_player_stats = gt_winning_player_stats[gt_winning_player_stats['matches_played'] != 0 ]
player_team['KA Pollard']
[{'Mumbai Indians': ['2017',
'2018',
'2019',
'2020',
'2021',
'2022',
'2010',
'2011',
'2012',
'2013',
'2014',
'2015',
'2016']}]
winning_years['Mumbai Indians']
['2017', '2019', '2020', '2013', '2015']
formula_scatterplot(mi_winning_player_stats, 'formula_batter', 'runs_scored',
'scores_above_30', 'batting_strike_rate', 'strike_rate_30',
'six_scored', 'matches_played', ['player_name', 'innings_batted', 'runs_scored', 'scores_above_30'])
player_team['PA Patel']
[{'Sunrisers Hyderabad': ['2013']},
{'Royal Challengers Bangalore': ['2018', '2019', '2014']},
{'Mumbai Indians': ['2017', '2015', '2016']},
{'Chennai Super Kings': ['2008', '2009', '2010']},
{'Deccan Chargers': ['2012']},
{'Kochi Tuskers Kerala': ['2011']}]
formula_scatterplot(mi_winning_player_stats, 'formula_bowler', 'wickets_taken',
'bowling_eco', 'wickets_taken', 'six_given',
'multi_wickets', 'matches_played', ['player_name', 'innings_bowled', 'bowling_eco'])
mi_years_around_wins = ['2012', '2014', '2016', '2018', '2021']
mi_winning_player_stats_2012_14_16_18_21 = all_players_stats_year_list(all_players, all_data, all_players_year_df, mi_years_around_wins)
formula_scatterplot(mi_winning_player_stats_2012_14_16_18_21, 'formula_batter', 'runs_scored',
'scores_above_30', 'batting_strike_rate', 'strike_rate_30',
'six_scored', 'matches_played', ['player_name', 'innings_batted', 'runs_scored', 'scores_above_30'])
player_team['GJ Maxwell']
[{'Royal Challengers Bangalore': ['2021', '2022', '2023']},
{'Mumbai Indians': ['2013']},
{'Kings XI Punjab': ['2017', '2020', '2014', '2015', '2016']},
{'Delhi Daredevils': ['2018', '2012']},
{'Royal Challengers Bengaluru': ['2024']}]
formula_scatterplot(mi_winning_player_stats_2012_14_16_18_21, 'formula_bowler', 'wickets_taken',
'bowling_eco', 'wickets_taken', 'six_given',
'multi_wickets', 'matches_played', ['player_name', 'innings_bowled', 'bowling_eco'])
winning_years['Chennai Super Kings']
['2018', '2021', '2023', '2010', '2011']
csk_years_around_wins = ['2012', '2013', '2014', '2015', '2016']
csk_winning_player_stats_2012_13_14_15_16 = all_players_stats_year_list(all_players, all_data, all_players_year_df, csk_years_around_wins)
formula_scatterplot(csk_winning_player_stats, 'formula_batter', 'runs_scored',
'scores_above_30', 'batting_strike_rate', 'strike_rate_30',
'six_scored', 'matches_played', ['player_name', 'innings_batted', 'batting_strike_rate', 'runs_scored', 'scores_above_30'])
formula_scatterplot(csk_winning_player_stats, 'formula_bowler', 'wickets_taken',
'bowling_eco', 'wickets_taken', 'six_given',
'multi_wickets', 'matches_played', ['player_name', 'innings_bowled', 'bowling_eco',
'wickets_taken'])
formula_scatterplot(csk_winning_player_stats_2012_13_14_15_16, 'formula_batter', 'runs_scored',
'scores_above_30', 'batting_strike_rate', 'strike_rate_30',
'six_scored', 'matches_played', ['player_name', 'innings_batted', 'runs_scored', 'scores_above_30'])
player_team['SR Watson']
[{'Royal Challengers Bangalore': ['2017', '2016']},
{'Chennai Super Kings': ['2018', '2019', '2020']},
{'Rajasthan Royals': ['2008',
'2010',
'2011',
'2012',
'2013',
'2014',
'2015']}]
formula_scatterplot(csk_winning_player_stats_2012_13_14_15_16, 'formula_bowler', 'wickets_taken',
'bowling_eco', 'wickets_taken', 'six_given',
'multi_wickets', 'matches_played', ['player_name', 'innings_bowled', 'bowling_eco',
'wickets_taken'])
player_team['Harbhajan Singh']
[{'Mumbai Indians': ['2017',
'2008',
'2009',
'2010',
'2011',
'2012',
'2013',
'2014',
'2015',
'2016']},
{'Kolkata Knight Riders': ['2021']},
{'Chennai Super Kings': ['2018', '2019']}]
winning_years['Kolkata Knight Riders']
['2024', '2012', '2014']
formula_scatterplot(kkr_winning_player_stats, 'formula_batter', 'runs_scored',
'scores_above_30', 'batting_strike_rate', 'runs_scored',
'six_scored', 'matches_played', ['player_name', 'innings_batted', 'runs_scored', 'scores_above_30'])
formula_scatterplot(kkr_winning_player_stats, 'formula_bowler', 'wickets_taken',
'bowling_eco', 'wickets_taken', 'six_given',
'multi_wickets', 'matches_played', ['player_name', 'innings_bowled', 'bowling_eco',
'wickets_taken'])
winning_years['Rajasthan Royals']
['2008']
formula_scatterplot(rr_winning_player_stats, 'formula_batter', 'runs_scored',
'scores_above_30', 'batting_strike_rate', 'runs_scored',
'six_scored', 'matches_played', ['player_name', 'innings_batted', 'runs_scored', 'scores_above_30'])
formula_scatterplot(rr_winning_player_stats, 'formula_bowler', 'wickets_taken',
'bowling_eco', 'wickets_taken', 'six_given',
'multi_wickets', 'matches_played', ['player_name', 'innings_bowled', 'bowling_eco',
'wickets_taken'])
winning_years['Gujarat Titans']
['2022']
formula_scatterplot(gt_winning_player_stats, 'formula_batter', 'runs_scored',
'scores_above_30', 'batting_strike_rate', 'runs_scored',
'six_scored', 'matches_played', ['player_name', 'innings_batted','batting_strike_rate', 'runs_scored', 'scores_above_30'])
formula_scatterplot(gt_winning_player_stats, 'formula_bowler', 'wickets_taken',
'bowling_eco', 'wickets_taken', 'six_given',
'multi_wickets', 'matches_played', ['player_name', 'innings_bowled', 'bowling_eco',
'wickets_taken'])
venue_data = []
venue_dict = {}
for data in all_data:
venue_data.append(data['info']['venue'])
venue_data = np.unique(venue_data)
for venue in venue_data:
runs_team_1 = []
runs_team_2 = []
wickets_team_1 = []
wickets_team_2 = []
boundaries_team_1 = []
boundaries_team_2 = []
runs_team_1_dict = {}
runs_team_2_dict = {}
wickets_team_1_dict = {}
wickets_team_2_dict = {}
boundaries_1_dict = {}
boundaries_2_dict = {}
for data in all_data:
runs_team1 = 0
runs_team2 = 0
wickets_team1 = 0
wickets_team2 = 0
boundaries_1 = 0
boundaries_2 = 0
#for team in all_teams:
if venue == data['info']['venue'].split(',')[0]:
#inning = 0
for over in data['innings'][0]['overs']:
for delivery in over['deliveries']:
runs_team1 += delivery['runs']['total']
try:
if delivery['wickets']:
wickets_team1 += 1
except:
None
try:
if delivery['runs']['batter'] == 4: boundaries_1 += 1
elif delivery['runs']['batter'] == 6: boundaries_1 += 1
except:
None
runs_team_1.append(runs_team1)
wickets_team_1.append(wickets_team1)
boundaries_team_1.append(boundaries_1)
runs_team_1_dict['runs_team1'] = runs_team_1
wickets_team_1_dict['wickets_team_1'] = wickets_team_1
boundaries_1_dict['boundaries_1'] = boundaries_team_1
try:
for over in data['innings'][1]['overs']:
for delivery in over['deliveries']:
runs_team2 += delivery['runs']['total']
try:
if delivery['wickets']:
wickets_team2 += 1
except:
None
try:
if delivery['runs']['batter'] == 4: boundaries_2 += 1
elif delivery['runs']['batter'] == 6: boundaries_2 += 1
except:
None
runs_team_2.append(runs_team2)
wickets_team_2.append(wickets_team2)
boundaries_team_2.append(boundaries_2)
runs_team_2_dict['runs_team2'] = runs_team_2
wickets_team_2_dict['wickets_team_2'] = wickets_team_2
boundaries_2_dict['boundaries_2'] = boundaries_team_2
except:
runs_team_2.append(0)
wickets_team_2.append(0)
boundaries_team_2.append(0)
runs_team_2_dict['runs_team2'] = runs_team_2
wickets_team_2_dict['wickets_team_2'] = wickets_team_2
boundaries_2_dict['boundaries'] = boundaries_team_2
venue_dict[venue] = [runs_team_1_dict, wickets_team_1_dict, runs_team_2_dict, wickets_team_2_dict, boundaries_1_dict, boundaries_2_dict]
all_data[0]['innings'][0]['overs'][0]['deliveries'][2]['runs']['batter']
4
for key, val in venue_dict.items():
print(key, len(val[0]['runs_team1']))
Arun Jaitley Stadium 30 Barabati Stadium 7 Brabourne Stadium 27 Buffalo Park 3 De Beers Diamond Oval 3 Dr DY Patil Sports Academy 37 Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket Stadium 15 Dubai International Cricket Stadium 46 Eden Gardens 93 Feroz Shah Kotla 60 Green Park 4 Himachal Pradesh Cricket Association Stadium 13 Holkar Cricket Stadium 9 JSCA International Stadium Complex 7 Kingsmead 15 M Chinnaswamy Stadium 79 M.Chinnaswamy Stadium 15 MA Chidambaram Stadium 85 Maharashtra Cricket Association Stadium 35 Nehru Stadium 5 New Wanderers Stadium 8 Newlands 7 OUTsurance Oval 2 Punjab Cricket Association IS Bindra Stadium 26 Rajiv Gandhi International Stadium 77 Saurashtra Cricket Association Stadium 10 Sawai Mansingh Stadium 57 Shaheed Veer Narayan Singh International Stadium 6 Sharjah Cricket Stadium 28 Sheikh Zayed Stadium 29 St George's Park 7 Subrata Roy Sahara Stadium 16 SuperSport Park 12 Wankhede Stadium 118
venue_data_list = []
for data in all_data:
venue_data_list.append(data['info']['venue'])
{i:venue_data_list.count(i) for i in venue_data_list}
{'Rajiv Gandhi International Stadium, Uppal': 49,
'Maharashtra Cricket Association Stadium': 22,
'Saurashtra Cricket Association Stadium': 10,
'Holkar Cricket Stadium': 9,
'M.Chinnaswamy Stadium': 15,
'Wankhede Stadium': 73,
'Eden Gardens': 77,
'M Chinnaswamy Stadium': 65,
'Feroz Shah Kotla': 60,
'Punjab Cricket Association IS Bindra Stadium, Mohali': 11,
'Green Park': 4,
'Punjab Cricket Association IS Bindra Stadium': 10,
'Rajiv Gandhi International Stadium': 15,
'MA Chidambaram Stadium': 9,
'Sawai Mansingh Stadium': 47,
'Arun Jaitley Stadium': 14,
'Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket Stadium': 13,
'Sheikh Zayed Stadium': 29,
'Dubai International Cricket Stadium': 46,
'Sharjah Cricket Stadium': 28,
'MA Chidambaram Stadium, Chepauk, Chennai': 28,
'Wankhede Stadium, Mumbai': 45,
'Narendra Modi Stadium, Ahmedabad': 24,
'Arun Jaitley Stadium, Delhi': 16,
'Zayed Cricket Stadium, Abu Dhabi': 8,
'Brabourne Stadium, Mumbai': 17,
'Dr DY Patil Sports Academy, Mumbai': 20,
'Maharashtra Cricket Association Stadium, Pune': 13,
'Eden Gardens, Kolkata': 16,
'Punjab Cricket Association IS Bindra Stadium, Mohali, Chandigarh': 5,
'Bharat Ratna Shri Atal Bihari Vajpayee Ekana Cricket Stadium, Lucknow': 14,
'Rajiv Gandhi International Stadium, Uppal, Hyderabad': 13,
'M Chinnaswamy Stadium, Bengaluru': 14,
'Barsapara Cricket Stadium, Guwahati': 3,
'Sawai Mansingh Stadium, Jaipur': 10,
'Himachal Pradesh Cricket Association Stadium, Dharamsala': 4,
'Maharaja Yadavindra Singh International Cricket Stadium, Mullanpur': 5,
'Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket Stadium, Visakhapatnam': 2,
'Punjab Cricket Association Stadium, Mohali': 35,
'MA Chidambaram Stadium, Chepauk': 48,
'Dr DY Patil Sports Academy': 17,
'Newlands': 7,
"St George's Park": 7,
'Kingsmead': 15,
'SuperSport Park': 12,
'Buffalo Park': 3,
'New Wanderers Stadium': 8,
'De Beers Diamond Oval': 3,
'OUTsurance Oval': 2,
'Brabourne Stadium': 10,
'Sardar Patel Stadium, Motera': 12,
'Barabati Stadium': 7,
'Vidarbha Cricket Association Stadium, Jamtha': 3,
'Himachal Pradesh Cricket Association Stadium': 9,
'Nehru Stadium': 5,
'Subrata Roy Sahara Stadium': 16,
'Shaheed Veer Narayan Singh International Stadium': 6,
'JSCA International Stadium Complex': 7}
import re
all_std = {}
for key, val in stadium_count_dict.items():
stadium_name = key.split(',')[0]
values = []
for k, v in venue_dict.items():
if re.match(stadium_name, k.split(',')[0]):
values.append(v)
all_std[stadium_name] = values
def create_venue_df(venue_dict, stadium_name):
df = pd.DataFrame(venue_dict[stadium_name][0])
df['match'] =
df['runs_team2'] = list(venue_dict[stadium_name][2].values())[0]
df['wickets_team1'] = list(venue_dict[stadium_name][1].values())[0]
df['wickets_team2'] = list(venue_dict[stadium_name][3].values())[0]
df['boundaries_1'] = list(venue_dict[stadium_name][4].values())[0]
df['boundaries_2'] = list(venue_dict[stadium_name][5].values())[0]
df['team_won'] = np.nan
df['wickets_high_inni'] = np.nan
for i in range(len(df['runs_team1'])):
if df['runs_team1'].iloc[i] > df['runs_team2'].iloc[i]:
df['team_won'].iloc[i] = '1'
elif df['runs_team2'].iloc[i] > df['runs_team1'].iloc[i]:
df['team_won'].iloc[i] = '2'
else:
df['team_won'].iloc[i] = '3'
for i in range(len(df['wickets_team1'])):
if df['wickets_team1'].iloc[i] > df['wickets_team2'].iloc[i]:
df['wickets_high_inni'].iloc[i] = '1'
elif df['wickets_team2'].iloc[i] > df['wickets_team1'].iloc[i]:
df['wickets_high_inni'].iloc[i] = '2'
else:
df['wickets_high_inni'].iloc[i] = '3'
return df
Cell In[188], line 3 df['match'] = ^ SyntaxError: invalid syntax
for key, values in venue_dict.items():
arun_jaitley = create_venue_df(venue_dict, key)
print(arun_jaitley)
runs_team1 runs_team2 wickets_team1 wickets_team2 boundaries_1 \
0 143 139 8 8 14
1 219 164 4 9 31
2 196 146 6 5 29
3 187 191 5 1 28
4 181 187 4 5 23
5 162 128 5 6 19
6 174 163 4 10 20
7 147 150 6 4 18
8 185 185 8 6 26
9 129 131 8 5 13
10 168 128 5 9 20
11 163 166 7 5 21
12 187 171 5 7 26
13 115 121 9 5 11
14 171 173 3 3 19
15 171 172 4 3 21
16 218 219 4 6 30
17 220 165 3 8 29
18 162 163 8 4 20
19 172 173 10 4 25
20 127 128 10 6 16
21 197 188 6 6 26
22 181 187 4 3 22
23 167 136 7 8 21
24 223 146 3 9 31
25 266 199 7 10 40
26 224 220 4 8 34
27 257 247 4 9 39
28 221 201 8 8 34
29 208 189 4 9 28
boundaries_2 team_won wickets_high_inni
0 14 1 3
1 21 1 2
2 21 1 1
3 26 2 1
4 24 2 2
5 13 1 2
6 19 1 2
7 19 2 1
8 26 3 1
9 16 2 1
10 12 1 2
11 21 2 1
12 20 1 2
13 13 2 1
14 26 2 3
15 19 2 1
16 30 2 2
17 20 1 2
18 20 2 1
19 24 2 1
20 17 2 1
21 22 1 3
22 27 2 1
23 19 1 2
24 17 1 2
25 31 1 2
26 32 1 2
27 35 1 2
28 29 1 3
29 26 1 2
runs_team1 runs_team2 wickets_team1 wickets_team2 boundaries_1 \
0 170 164 8 8 20
1 171 161 6 9 24
2 126 127 7 5 11
3 186 173 4 5 22
4 231 187 4 6 37
5 149 150 8 1 19
6 141 142 5 4 15
boundaries_2 team_won wickets_high_inni
0 23 1 3
1 19 1 2
2 13 2 1
3 21 1 2
4 23 1 2
5 19 2 1
6 17 2 1
runs_team1 runs_team2 wickets_team1 wickets_team2 boundaries_1 \
0 177 179 5 6 24
1 210 211 7 4 32
2 180 126 8 10 23
3 189 190 9 4 27
4 215 171 5 10 29
5 175 176 8 3 24
6 199 181 4 9 28
7 217 210 5 10 29
8 115 119 10 1 16
9 68 72 10 1 6
10 170 174 6 4 22
11 207 186 3 8 30
12 177 172 6 5 22
13 209 155 9 9 30
14 178 154 6 8 26
15 150 151 6 5 20
16 212 208 7 7 29
17 151 155 9 3 18
18 155 156 3 3 17
19 180 181 2 5 21
20 163 164 10 6 15
21 178 115 5 10 21
22 183 144 4 7 21
23 173 176 8 3 23
24 189 175 2 7 25
25 201 194 4 7 28
26 199 190 6 9 26
boundaries_2 team_won wickets_high_inni
0 25 2 2
1 27 2 1
2 16 1 2
3 26 2 1
4 22 1 2
5 23 2 1
6 23 1 2
7 27 1 2
8 20 2 1
9 12 2 1
10 22 2 1
11 27 1 2
12 24 1 1
13 18 1 3
14 15 1 2
15 18 2 1
16 29 1 3
17 20 2 1
18 22 2 3
19 22 2 2
20 18 2 1
21 9 1 2
22 13 1 2
23 23 2 1
24 21 1 2
25 28 1 2
26 26 1 2
runs_team1 runs_team2 wickets_team1 wickets_team2 boundaries_1 \
0 148 139 6 6 14
1 178 100 3 10 21
2 116 118 10 3 10
boundaries_2 team_won wickets_high_inni
0 13 1 3
1 12 1 2
2 13 2 1
runs_team1 runs_team2 wickets_team1 wickets_team2 boundaries_1 \
0 168 169 5 7 18
1 140 141 7 3 16
2 166 113 7 10 16
boundaries_2 team_won wickets_high_inni
0 17 2 2
1 18 2 1
2 11 1 2
runs_team1 runs_team2 wickets_team1 wickets_team2 boundaries_1 \
0 205 208 2 5 22
1 128 132 10 7 18
2 193 170 8 8 28
3 169 157 7 9 20
4 149 155 3 4 19
5 154 155 7 2 17
6 162 168 7 3 18
7 216 193 4 9 29
8 192 155 4 9 29
9 151 152 10 3 20
10 181 163 6 8 21
11 155 156 7 7 17
12 156 148 9 8 15
13 158 161 6 5 19
14 143 145 8 2 13
15 208 117 6 10 28
16 165 113 9 10 22
17 160 161 6 2 20
18 159 142 7 9 18
19 210 208 0 8 27
20 154 155 7 0 19
21 162 133 8 10 22
22 103 104 10 3 11
23 163 164 5 7 16
24 161 150 4 7 19
25 172 131 7 10 27
26 184 149 5 9 21
27 142 104 7 10 15
28 82 86 10 1 10
29 168 146 5 9 19
30 112 113 8 3 13
31 148 151 8 6 17
32 187 190 5 7 24
33 141 145 6 2 17
34 160 139 7 7 19
35 136 138 9 4 17
36 118 119 7 3 11
boundaries_2 team_won wickets_high_inni
0 25 2 2
1 16 2 1
2 21 1 3
3 17 1 2
4 16 2 2
5 19 2 1
6 19 2 1
7 24 1 2
8 19 1 2
9 16 2 1
10 20 1 2
11 19 2 3
12 20 1 1
13 19 2 1
14 20 2 1
15 17 1 2
16 11 1 2
17 20 2 1
18 18 1 2
19 31 1 2
20 24 2 1
21 14 1 2
22 10 2 1
23 17 2 2
24 15 1 2
25 15 1 2
26 15 1 2
27 11 1 2
28 11 2 1
29 17 1 2
30 18 2 1
31 17 2 1
32 25 2 2
33 14 2 1
34 15 1 3
35 14 2 1
36 14 2 1
runs_team1 runs_team2 wickets_team1 wickets_team2 boundaries_1 \
0 162 165 8 8 19
1 147 151 9 4 17
2 191 171 5 6 25
3 272 166 7 10 40
4 193 119 6 10 22
5 138 142 9 5 14
6 127 131 5 4 15
7 167 163 4 8 18
8 176 101 4 4 22
9 177 92 3 10 24
10 137 133 8 8 15
11 124 127 9 3 13
12 206 126 4 10 27
13 121 76 6 1 13
14 172 173 7 6 21
boundaries_2 team_won wickets_high_inni
0 23 2 3
1 19 2 1
2 20 1 2
3 23 1 2
4 15 1 2
5 16 2 1
6 15 2 1
7 18 1 2
8 11 1 3
9 11 1 2
10 14 1 3
11 14 2 1
12 16 1 2
13 11 1 1
14 22 2 1
runs_team1 runs_team2 wickets_team1 wickets_team2 boundaries_1 \
0 157 157 8 8 19
1 126 114 7 10 9
2 174 137 6 9 20
3 158 163 4 5 15
4 206 109 3 10 28
5 178 181 4 0 20
6 164 157 5 5 21
7 176 176 6 6 24
8 154 156 6 2 16
9 196 137 4 9 25
10 177 179 6 3 22
11 219 131 2 10 30
12 169 132 4 8 15
13 167 147 6 8 21
14 191 131 7 9 28
15 163 153 5 10 17
16 110 111 9 1 10
17 172 178 5 4 25
18 175 131 3 7 22
19 201 132 6 10 24
20 161 148 7 8 16
21 145 150 6 2 13
22 164 167 5 5 19
23 201 201 3 5 27
24 200 143 5 8 27
25 156 157 7 5 19
26 134 139 6 4 15
27 164 167 5 3 19
28 164 166 5 3 18
29 165 168 7 5 19
30 149 153 9 3 17
31 156 136 7 8 18
32 134 139 9 2 13
33 165 111 6 10 21
34 115 119 8 4 12
35 136 139 5 7 12
36 185 183 10 4 26
37 172 173 5 6 21
38 192 165 3 9 22
39 115 116 9 3 8
40 166 167 5 6 18
41 140 133 6 10 13
42 184 180 1 4 24
43 141 142 7 3 16
44 124 127 8 5 12
45 172 157 5 7 16
boundaries_2 team_won wickets_high_inni
0 18 3 3
1 14 1 2
2 12 1 2
3 18 2 2
4 13 1 2
5 26 2 1
6 19 1 3
7 22 3 3
8 21 2 1
9 13 1 2
10 15 2 1
11 16 1 2
12 13 1 2
13 15 1 2
14 15 1 2
15 17 1 2
16 14 2 1
17 22 2 1
18 13 1 2
19 16 1 2
20 18 1 2
21 19 2 1
22 21 2 3
23 22 3 2
24 19 1 2
25 21 2 1
26 19 2 1
27 20 2 1
28 18 2 1
29 17 2 1
30 20 2 1
31 15 1 2
32 18 2 1
33 12 1 2
34 17 2 1
35 16 2 2
36 21 1 1
37 22 2 2
38 20 1 2
39 13 2 1
40 19 2 2
41 13 1 2
42 24 1 2
43 16 2 1
44 13 2 1
45 20 1 2
runs_team1 runs_team2 wickets_team1 wickets_team2 boundaries_1 \
0 170 171 9 2 24
1 172 155 6 6 20
2 187 188 5 6 27
3 131 49 10 10 16
4 160 161 6 3 18
.. ... ... ... ... ...
88 164 157 3 9 18
89 158 164 4 5 19
90 103 66 6 2 9
91 183 186 5 1 22
92 171 149 6 8 18
boundaries_2 team_won wickets_high_inni
0 25 2 1
1 21 1 3
2 28 2 2
3 8 1 3
4 24 2 1
.. ... ... ...
88 18 1 2
89 26 2 2
90 9 1 1
91 25 2 1
92 15 1 2
[93 rows x 8 columns]
runs_team1 runs_team2 wickets_team1 wickets_team2 boundaries_1 \
0 188 137 6 9 28
1 168 169 7 6 23
2 185 189 3 4 26
3 208 214 7 3 27
4 212 66 3 10 26
5 168 161 8 7 20
6 161 151 6 10 18
7 129 132 8 1 17
8 191 181 5 5 26
9 187 188 5 6 25
10 194 182 4 9 24
11 118 94 4 3 16
12 176 179 8 5 25
13 218 120 7 9 28
14 185 190 6 5 26
15 177 137 4 9 22
16 188 121 6 10 23
17 184 147 5 9 25
18 111 112 10 3 8
19 145 134 7 7 18
20 95 99 10 2 11
21 168 152 4 7 23
22 231 202 4 6 32
23 160 161 6 7 18
24 148 131 7 9 16
25 140 141 6 3 18
26 56 0 3 0 5
27 110 111 8 2 10
28 157 162 8 5 18
29 192 172 3 7 26
30 207 170 5 9 31
31 152 151 6 3 18
32 153 154 9 4 17
33 136 140 8 5 20
34 215 194 1 9 32
35 165 160 7 6 19
36 114 115 8 7 10
37 169 83 4 10 22
38 161 165 4 1 19
39 183 179 4 7 25
40 120 121 7 5 12
41 192 144 1 10 24
42 132 135 7 6 15
43 152 156 5 3 18
44 178 181 5 2 24
45 160 161 5 2 19
46 143 44 7 2 15
47 164 165 7 6 21
48 184 186 3 7 22
49 146 147 8 4 18
50 190 153 4 9 24
51 95 99 10 0 9
52 118 119 8 1 12
53 111 113 9 2 13
54 164 154 4 7 17
55 172 171 6 5 21
56 186 159 8 10 23
57 162 166 7 3 17
58 162 140 8 8 21
59 162 163 7 6 25
boundaries_2 team_won wickets_high_inni
0 14 1 2
1 18 2 1
2 25 2 2
3 28 2 1
4 8 1 2
5 19 1 1
6 19 1 2
7 19 2 1
8 23 1 3
9 25 2 2
10 24 1 2
11 12 1 1
12 26 2 1
13 16 1 2
14 28 2 1
15 14 1 2
16 15 1 2
17 15 1 2
18 13 2 1
19 14 1 3
20 9 2 1
21 17 1 2
22 32 1 2
23 22 2 2
24 14 1 2
25 20 2 1
26 0 1 1
27 14 2 1
28 21 2 1
29 21 1 2
30 23 1 2
31 18 1 1
32 15 2 1
33 21 2 1
34 26 1 2
35 16 1 1
36 14 2 1
37 6 1 2
38 25 2 1
39 26 1 2
40 16 2 1
41 17 1 2
42 18 2 1
43 19 2 1
44 26 2 1
45 17 2 1
46 4 1 1
47 19 2 1
48 23 2 2
49 20 2 1
50 19 1 2
51 16 2 1
52 15 2 1
53 15 2 1
54 19 1 2
55 20 1 1
56 17 1 2
57 17 2 1
58 11 1 3
59 22 2 1
runs_team1 runs_team2 wickets_team1 wickets_team2 boundaries_1 \
0 195 197 5 8 26
1 154 158 10 2 21
2 124 125 8 4 14
3 172 173 8 4 24
boundaries_2 team_won wickets_high_inni
0 35 2 2
1 23 2 1
2 15 2 1
3 29 2 1
runs_team1 runs_team2 wickets_team1 wickets_team2 boundaries_1 \
0 213 198 2 8 31
1 187 189 5 6 26
2 167 139 9 9 20
3 241 181 7 10 35
4 174 178 3 5 27
5 192 195 3 4 27
6 170 141 6 8 26
7 232 121 2 10 32
8 198 116 2 10 23
9 120 123 7 4 15
10 141 145 8 4 20
11 171 164 4 7 25
12 183 133 8 9 29
boundaries_2 team_won wickets_high_inni
0 28 1 2
1 27 2 2
2 17 1 3
3 30 1 2
4 24 2 2
5 29 2 2
6 19 1 2
7 16 1 2
8 16 1 2
9 20 2 1
10 22 2 1
11 22 1 2
12 15 1 2
runs_team1 runs_team2 wickets_team1 wickets_team2 boundaries_1 \
0 163 164 6 4 18
1 148 150 4 2 17
2 198 199 4 2 26
3 174 176 6 4 21
4 152 155 9 4 17
5 245 214 6 8 39
6 88 92 10 0 10
7 178 181 7 4 19
8 97 98 10 2 13
boundaries_2 team_won wickets_high_inni
0 20 2 1
1 20 2 1
2 28 2 1
3 24 2 1
4 19 2 1
5 28 1 2
6 15 2 1
7 27 2 1
8 16 2 1
runs_team1 runs_team2 wickets_team1 wickets_team2 boundaries_1 \
0 115 116 9 5 7
1 170 163 4 7 20
2 148 114 3 9 19
3 148 149 8 5 17
4 138 142 4 5 14
5 185 189 3 4 25
6 139 140 8 7 16
boundaries_2 team_won wickets_high_inni
0 14 2 1
1 19 1 2
2 13 1 2
3 14 2 1
4 16 2 2
5 24 2 2
6 12 2 1
runs_team1 runs_team2 wickets_team1 wickets_team2 boundaries_1 \
0 158 79 6 1 19
1 189 180 5 9 24
2 168 173 9 3 21
3 168 156 9 7 19
4 165 169 6 4 22
5 139 143 6 5 14
6 119 116 8 7 6
7 145 137 9 7 18
8 211 133 4 8 33
9 154 157 3 1 18
10 173 161 7 10 22
11 129 132 10 8 15
12 145 143 7 10 19
13 101 102 9 6 10
14 116 92 9 8 13
boundaries_2 team_won wickets_high_inni
0 9 1 1
1 19 1 2
2 17 2 1
3 17 1 1
4 20 2 1
5 15 2 1
6 8 1 1
7 15 1 1
8 15 1 2
9 19 2 1
10 20 1 2
11 15 2 1
12 12 1 2
13 11 2 1
14 6 1 1
runs_team1 runs_team2 wickets_team1 wickets_team2 boundaries_1 \
0 142 145 5 6 11
1 161 134 8 9 21
2 134 135 10 3 13
3 138 119 7 10 14
4 158 159 6 4 17
.. ... ... ... ... ...
74 151 153 4 4 17
75 248 104 3 10 37
76 211 120 3 9 35
77 158 159 10 6 20
78 208 200 7 7 30
boundaries_2 team_won wickets_high_inni
0 14 2 2
1 12 1 2
2 20 2 1
3 13 1 2
4 22 2 1
.. ... ... ...
74 17 2 3
75 11 1 2
76 16 1 2
77 19 2 1
78 25 1 3
[79 rows x 8 columns]
runs_team1 runs_team2 wickets_team1 wickets_team2 boundaries_1 \
0 157 142 8 9 21
1 155 159 10 6 18
2 217 198 4 6 28
3 174 176 5 4 23
4 205 207 8 5 25
5 175 176 4 5 21
6 167 153 7 7 21
7 218 204 6 3 32
8 187 181 8 5 26
9 205 206 3 5 27
10 149 152 8 6 15
11 161 160 7 8 20
12 202 185 4 7 27
13 62 41 7 1 9
14 175 178 7 6 23
boundaries_2 team_won wickets_high_inni
0 15 1 2
1 21 2 1
2 27 1 2
3 23 2 1
4 26 2 1
5 24 2 2
6 16 1 3
7 28 1 1
8 24 1 1
9 27 2 2
10 21 2 1
11 18 1 2
12 28 1 2
13 7 1 1
14 24 2 1
runs_team1 runs_team2 wickets_team1 wickets_team2 boundaries_1 \
0 202 205 6 5 28
1 70 71 10 3 4
2 175 167 5 8 22
3 160 138 3 5 18
4 108 111 9 3 14
.. ... ... ... ... ...
80 192 95 3 9 26
81 134 132 6 9 15
82 148 124 9 10 17
83 158 159 5 4 19
84 157 145 5 9 19
boundaries_2 team_won wickets_high_inni
0 23 2 1
1 7 2 1
2 18 1 2
3 12 1 2
4 11 2 1
.. ... ... ...
80 7 1 2
81 15 1 2
82 16 1 2
83 18 2 1
84 14 1 2
[85 rows x 8 columns]
runs_team1 runs_team2 wickets_team1 wickets_team2 boundaries_1 \
0 184 187 8 3 23
1 205 108 4 10 27
2 176 179 3 4 22
3 182 184 5 3 22
4 157 96 3 9 18
5 161 167 10 5 20
6 73 78 10 1 7
7 204 140 5 10 31
8 169 170 5 2 21
9 211 198 4 5 28
10 127 128 9 4 14
11 179 180 4 2 23
12 153 159 10 5 17
13 210 149 6 7 30
14 171 157 6 9 19
15 161 162 4 5 20
16 151 152 6 3 21
17 198 186 5 9 23
18 169 170 5 7 20
19 144 115 8 10 17
20 153 133 8 8 16
21 202 189 2 6 25
22 173 160 8 8 19
23 176 101 7 10 21
24 144 82 4 10 16
25 177 123 6 8 21
26 119 108 8 10 11
27 162 136 7 8 17
28 165 169 7 5 17
29 155 159 9 6 17
30 180 109 4 10 23
31 185 172 3 9 20
32 160 162 5 8 15
33 195 196 3 7 22
34 159 161 5 2 16
boundaries_2 team_won wickets_high_inni
0 24 2 1
1 11 1 2
2 24 2 2
3 24 2 1
4 7 1 2
5 20 2 1
6 7 2 1
7 13 1 2
8 20 2 1
9 26 1 2
10 16 2 1
11 24 2 1
12 18 2 1
13 17 1 2
14 21 1 2
15 24 2 2
16 17 2 1
17 26 1 2
18 21 2 2
19 13 1 2
20 17 1 3
21 22 1 2
22 20 1 3
23 14 1 2
24 10 1 2
25 13 1 2
26 12 1 2
27 17 1 2
28 22 2 1
29 25 2 1
30 11 1 2
31 20 1 2
32 19 2 2
33 25 2 2
34 20 2 1
runs_team1 runs_team2 wickets_team1 wickets_team2 boundaries_1 \
0 161 162 5 4 18
1 131 135 4 3 18
2 129 74 7 10 14
3 157 119 7 10 19
4 156 139 5 7 17
boundaries_2 team_won wickets_high_inni
0 19 2 1
1 19 2 1
2 9 1 2
3 12 1 2
4 17 1 2
runs_team1 runs_team2 wickets_team1 wickets_team2 boundaries_1 \
0 163 145 10 8 20
1 149 150 4 1 16
2 123 125 8 3 11
3 160 166 5 4 17
4 134 133 7 8 13
5 134 135 7 3 9
6 146 149 5 4 15
7 143 137 6 9 13
boundaries_2 team_won wickets_high_inni
0 19 1 1
1 18 2 1
2 14 2 1
3 20 2 1
4 16 1 2
5 13 2 1
6 20 2 1
7 15 1 2
runs_team1 runs_team2 wickets_team1 wickets_team2 boundaries_1 \
0 165 146 7 7 22
1 133 58 8 10 17
2 104 58 7 0 12
3 101 104 10 2 11
4 184 160 6 8 21
5 150 150 6 8 16
6 139 112 6 7 15
boundaries_2 team_won wickets_high_inni
0 16 1 3
1 3 1 2
2 9 1 1
3 15 2 1
4 21 1 2
5 16 3 2
6 9 1 2
runs_team1 runs_team2 wickets_team1 wickets_team2 boundaries_1 \
0 120 123 9 4 7
1 150 136 3 9 13
boundaries_2 team_won wickets_high_inni
0 16 2 1
1 14 1 2
runs_team1 runs_team2 wickets_team1 wickets_team2 boundaries_1 \
0 207 181 3 9 26
1 67 68 10 0 5
2 189 192 3 4 21
3 167 153 6 6 18
4 166 167 7 4 16
5 197 193 7 5 28
6 193 178 3 5 24
7 176 177 7 2 23
8 166 152 9 10 22
9 150 151 4 4 14
10 173 174 4 2 23
11 182 170 6 7 20
12 183 185 6 3 23
13 170 173 5 4 22
14 191 146 5 7 26
15 153 154 8 4 21
16 174 150 4 10 18
17 257 201 5 10 41
18 214 216 3 4 27
19 161 162 6 5 18
20 152 153 7 4 18
21 138 141 8 4 12
22 189 164 6 7 25
23 181 172 5 5 20
24 175 174 6 4 18
25 179 180 4 3 26
boundaries_2 team_won wickets_high_inni
0 29 1 2
1 10 2 1
2 23 2 2
3 18 1 3
4 23 2 1
5 23 1 1
6 16 1 2
7 22 2 1
8 20 1 2
9 16 2 3
10 23 2 1
11 15 1 2
12 25 2 1
13 24 2 1
14 20 1 2
15 18 2 1
16 19 1 2
17 26 1 2
18 32 2 2
19 24 2 1
20 19 2 1
21 18 2 1
22 16 1 2
23 18 1 3
24 21 1 1
25 22 2 1
runs_team1 runs_team2 wickets_team1 wickets_team2 boundaries_1 \
0 207 172 4 10 26
1 135 140 7 1 13
2 159 154 6 10 15
3 191 176 4 5 22
4 209 161 3 7 26
.. ... ... ... ... ...
72 143 146 6 5 15
73 118 94 8 3 9
74 194 179 5 6 27
75 126 129 6 5 11
76 146 150 8 3 16
boundaries_2 team_won wickets_high_inni
0 23 1 2
1 17 2 1
2 18 1 2
3 24 1 2
4 17 1 2
.. ... ... ...
72 18 2 1
73 13 1 1
74 20 1 2
75 15 2 1
76 19 2 1
[77 rows x 8 columns]
runs_team1 runs_team2 wickets_team1 wickets_team2 boundaries_1 \
0 183 184 4 0 26
1 171 172 8 3 21
2 213 192 2 7 30
3 188 162 7 7 26
4 153 153 9 10 19
5 163 164 5 3 19
6 135 137 8 0 12
7 180 182 2 4 22
8 154 131 10 9 16
9 149 150 7 2 16
boundaries_2 team_won wickets_high_inni
0 26 2 1
1 27 2 1
2 24 1 2
3 18 1 3
4 19 3 2
5 20 2 1
6 14 2 1
7 20 2 2
8 13 1 1
9 20 2 1
runs_team1 runs_team2 wickets_team1 wickets_team2 boundaries_1 \
0 153 60 5 4 17
1 160 163 8 3 19
2 167 168 7 7 20
3 151 140 7 6 17
4 158 143 8 7 17
5 176 177 4 6 18
6 164 134 5 10 17
7 184 170 4 9 24
8 158 164 4 3 21
9 139 140 3 2 15
10 151 155 7 6 18
11 161 162 5 5 17
12 191 193 6 4 26
13 160 161 8 3 13
14 154 144 7 6 18
15 202 170 5 6 27
16 118 119 10 1 12
17 214 217 2 6 30
18 171 59 5 10 19
19 193 173 4 6 21
20 185 173 5 5 25
21 183 189 3 4 21
22 196 199 3 7 24
23 179 183 9 1 22
24 166 168 8 4 25
25 196 151 7 10 27
26 109 110 10 2 13
27 140 141 8 2 18
28 156 159 7 7 15
29 197 132 1 9 24
30 145 146 7 5 15
31 153 157 6 1 17
32 174 137 5 8 23
33 130 132 6 5 15
34 151 152 6 4 20
35 159 160 4 1 15
36 109 111 10 2 8
37 94 95 8 3 6
38 143 144 7 4 17
39 196 133 3 10 25
40 146 151 6 1 15
41 191 160 4 9 26
42 164 142 5 10 19
43 196 197 2 5 25
44 189 143 3 7 23
45 141 144 6 4 17
46 126 127 6 6 14
47 170 125 4 9 22
48 162 163 6 0 19
49 144 125 6 10 19
50 124 126 10 4 14
51 179 92 3 10 22
52 144 146 9 2 18
53 171 173 6 6 20
54 178 182 4 5 25
55 154 155 4 1 21
56 141 144 4 5 16
boundaries_2 team_won wickets_high_inni
0 9 1 1
1 21 2 1
2 17 2 3
3 11 1 1
4 15 1 1
5 24 2 2
6 17 1 2
7 21 1 2
8 20 2 1
9 21 2 1
10 13 2 1
11 22 2 3
12 27 2 1
13 20 2 1
14 17 1 1
15 20 1 2
16 17 2 1
17 28 2 2
18 8 1 2
19 19 1 2
20 22 1 3
21 26 2 2
22 24 2 2
23 26 2 1
24 24 2 1
25 14 1 2
26 12 2 1
27 18 2 1
28 19 2 3
29 16 1 2
30 13 2 1
31 25 2 1
32 12 1 2
33 22 2 1
34 19 2 1
35 21 2 1
36 15 2 1
37 9 2 1
38 15 2 1
39 12 1 2
40 23 2 1
41 18 1 2
42 15 1 2
43 28 2 2
44 16 1 2
45 21 2 1
46 16 2 3
47 12 1 2
48 19 2 1
49 12 1 2
50 17 2 1
51 8 1 2
52 23 2 1
53 21 2 3
54 25 2 2
55 17 2 1
56 20 2 2
runs_team1 runs_team2 wickets_team1 wickets_team2 boundaries_1 \
0 164 149 5 4 20
1 136 137 7 3 18
2 163 157 4 4 15
3 119 120 6 4 13
4 158 161 7 4 18
5 138 139 8 4 12
boundaries_2 team_won wickets_high_inni
0 19 1 1
1 17 2 1
2 23 1 3
3 18 2 1
4 17 2 1
5 15 2 1
runs_team1 runs_team2 wickets_team1 wickets_team2 boundaries_1 \
0 149 151 8 0 18
1 216 200 7 6 26
2 184 138 8 10 22
3 120 121 7 5 10
4 179 185 4 5 23
5 228 210 4 8 32
6 149 150 9 2 20
7 114 116 9 0 12
8 223 226 2 6 31
9 171 177 6 2 16
10 208 174 5 7 29
11 194 112 2 9 22
12 164 158 7 6 20
13 134 139 7 4 11
14 127 130 9 7 14
15 90 94 9 2 9
16 171 85 4 10 19
17 125 120 7 7 10
18 129 132 8 6 14
19 156 157 6 4 17
20 138 139 7 6 12
21 135 136 5 7 12
22 145 146 4 2 13
23 191 193 5 3 27
24 193 121 6 10 27
25 150 148 7 5 13
26 125 126 7 4 13
27 145 146 5 5 13
boundaries_2 team_won wickets_high_inni
0 19 2 1
1 25 1 1
2 14 1 2
3 15 2 1
4 24 2 2
5 26 1 2
6 21 2 1
7 18 2 1
8 32 2 2
9 20 2 1
10 19 1 2
11 10 1 2
12 17 1 1
13 17 2 1
14 15 2 1
15 14 2 1
16 11 1 2
17 7 1 3
18 13 2 1
19 20 2 1
20 13 2 1
21 11 2 2
22 20 2 1
23 23 2 1
24 10 1 2
25 19 1 1
26 12 2 1
27 17 2 3
runs_team1 runs_team2 wickets_team1 wickets_team2 boundaries_1 \
0 162 166 9 5 19
1 84 85 8 2 8
2 194 135 6 9 30
3 164 166 6 5 24
4 167 157 10 5 19
5 191 143 4 8 27
6 152 154 7 4 16
7 153 154 6 1 17
8 195 146 5 9 22
9 193 136 4 10 26
10 163 163 5 6 21
11 154 158 6 2 17
12 164 162 6 5 20
13 148 149 5 2 18
14 162 166 4 5 18
15 162 147 4 7 15
16 125 126 5 3 13
17 185 186 4 3 23
18 195 196 5 2 26
19 142 145 4 3 12
20 131 132 7 4 11
21 189 172 3 8 21
22 163 122 5 7 19
23 205 206 4 4 25
24 133 135 6 6 8
25 177 84 7 9 19
26 70 71 10 4 7
27 132 109 9 10 14
28 152 152 5 8 18
boundaries_2 team_won wickets_high_inni
0 21 2 1
1 11 2 1
2 14 1 2
3 22 2 1
4 21 1 1
5 13 1 2
6 16 2 1
7 15 2 1
8 20 1 2
9 17 1 2
10 20 3 2
11 19 2 1
12 18 1 1
13 23 2 1
14 21 2 2
15 14 1 2
16 14 2 1
17 27 2 1
18 28 2 1
19 18 2 1
20 14 2 1
21 21 1 2
22 9 1 2
23 31 2 3
24 15 2 3
25 7 1 2
26 8 2 1
27 11 1 2
28 14 3 2
runs_team1 runs_team2 wickets_team1 wickets_team2 boundaries_1 \
0 179 87 5 10 21
1 149 150 7 4 13
2 187 95 6 9 21
3 141 142 5 7 16
4 153 154 3 4 19
5 157 141 2 7 14
6 147 151 5 3 15
boundaries_2 team_won wickets_high_inni
0 10 1 2
1 14 2 1
2 10 1 2
3 17 2 2
4 17 2 2
5 12 1 2
6 14 2 1
runs_team1 runs_team2 wickets_team1 wickets_team2 boundaries_1 \
0 166 144 6 8 18
1 155 156 5 3 17
2 146 148 2 2 16
3 177 159 4 7 19
4 120 119 9 6 12
5 125 126 6 3 7
6 173 138 3 9 22
7 136 102 4 8 14
8 162 144 4 8 20
9 99 100 9 2 11
10 145 148 5 3 18
11 164 127 3 9 18
12 187 170 3 9 23
13 152 106 6 10 17
14 112 116 8 5 8
15 172 134 5 9 22
boundaries_2 team_won wickets_high_inni
0 15 1 2
1 17 2 1
2 19 2 3
3 18 1 2
4 13 1 1
5 17 2 1
6 16 1 2
7 3 1 2
8 17 1 2
9 15 2 1
10 19 2 1
11 13 1 2
12 20 1 2
13 8 1 2
14 14 2 1
15 14 1 2
runs_team1 runs_team2 wickets_team1 wickets_team2 boundaries_1 \
0 143 147 7 5 17
1 148 150 9 4 16
2 164 126 5 9 21
3 145 126 6 8 14
4 105 107 10 3 9
5 185 174 3 3 28
6 173 176 4 4 22
7 119 122 9 2 13
8 188 189 3 3 22
9 165 166 8 6 20
10 170 158 4 6 20
11 153 154 8 4 17
boundaries_2 team_won wickets_high_inni
0 18 2 1
1 19 2 1
2 13 1 2
3 14 1 2
4 13 2 1
5 20 1 3
6 22 2 3
7 16 2 1
8 25 2 3
9 23 2 1
10 15 1 2
11 21 2 1
runs_team1 runs_team2 wickets_team1 wickets_team2 boundaries_1 \
0 178 180 7 6 20
1 158 159 8 6 21
2 176 177 4 4 18
3 142 128 8 7 15
4 160 157 6 8 20
.. ... ... ... ... ...
113 187 162 6 10 23
114 121 126 8 1 15
115 143 147 8 7 16
116 170 171 7 4 19
117 174 178 5 4 20
boundaries_2 team_won wickets_high_inni
0 24 2 1
1 22 2 1
2 19 2 3
3 14 1 1
4 21 1 2
.. ... ... ...
113 18 1 2
114 16 2 1
115 17 2 1
116 24 2 1
117 27 2 1
[118 rows x 8 columns]
arun_jaitley = create_venue_df(venue_dict, 'Arun Jaitley Stadium')
feroz_shah_kotla = create_venue_df(venue_dict, 'Feroz Shah Kotla')
arun_jaitley = pd.concat([feroz_shah_kotla, arun_jaitley], axis=0)
chidambaram_stadium = create_venue_df(venue_dict, 'MA Chidambaram Stadium')
rajiv_gandhi_stadium = create_venue_df(venue_dict, 'Rajiv Gandhi International Stadium')
chinnaswamy_stadium = create_venue_df(venue_dict, 'M Chinnaswamy Stadium')
wankhede_stadium = create_venue_df(venue_dict, 'Wankhede Stadium')
eden_gardens = create_venue_df(venue_dict, 'Eden Gardens')
sawai_mansingh = create_venue_df(venue_dict, 'Sawai Mansingh Stadium')
dubai_stadium = create_venue_df(venue_dict, 'Dubai International Cricket Stadium')
arun_jaitley.head(5)
| runs_team1 | runs_team2 | wickets_team1 | wickets_team2 | boundaries_1 | boundaries_2 | team_won | wickets_high_inni | |
|---|---|---|---|---|---|---|---|---|
| 0 | 188 | 137 | 6 | 9 | 28 | 14 | 1 | 2 |
| 1 | 168 | 169 | 7 | 6 | 23 | 18 | 2 | 1 |
| 2 | 185 | 189 | 3 | 4 | 26 | 25 | 2 | 2 |
| 3 | 208 | 214 | 7 | 3 | 27 | 28 | 2 | 1 |
| 4 | 212 | 66 | 3 | 10 | 26 | 8 | 1 | 2 |
print(arun_jaitley.value_counts('team_won'))
fig = px.scatter(arun_jaitley, x='runs_team1', y='runs_team2', color='team_won',
symbol='team_won', hover_data=['boundaries_1', 'boundaries_2'])
fig.update_traces(marker_size=10)
#fig.update_layout(scattermode='group')
fig.show()
team_won 1 46 2 43 3 1 dtype: int64
print(chidambaram_stadium.value_counts('team_won'))
fig = px.scatter(chidambaram_stadium, x='runs_team1', y='runs_team2', color='team_won',
symbol='team_won', hover_data=['boundaries_1', 'boundaries_2'])
fig.update_traces(marker_size=10)
#fig.update_layout(scattermode='group')
fig.show()
team_won 1 47 2 36 3 2 dtype: int64
print(chinnaswamy_stadium.value_counts('team_won'))
fig = px.scatter(chinnaswamy_stadium, x='runs_team1', y='runs_team2', color='team_won',
symbol='team_won', hover_data=['boundaries_1', 'boundaries_2'])
fig.update_traces(marker_size=10)
#fig.update_layout(scattermode='group')
fig.show()
team_won 2 41 1 37 3 1 dtype: int64
print(sawai_mansingh.value_counts('team_won'))
fig = px.scatter(sawai_mansingh, x='runs_team1', y='runs_team2', color='team_won',
symbol='team_won', hover_data=['boundaries_1', 'boundaries_2'])
fig.update_traces(marker_size=10)
#fig.update_layout(scattermode='group')
fig.show()
team_won 2 37 1 20 dtype: int64
print(rajiv_gandhi_stadium.value_counts('team_won'))
fig = px.scatter(rajiv_gandhi_stadium, x='runs_team1', y='runs_team2', color='team_won',
symbol='team_won', hover_data=['boundaries_1', 'boundaries_2'])
fig.update_traces(marker_size=10)
#fig.update_layout(scattermode='group')
fig.show()
team_won 2 40 1 36 3 1 dtype: int64
print(eden_gardens.value_counts('team_won'))
fig = px.scatter(eden_gardens, x='runs_team1', y='runs_team2', color='team_won',
symbol='team_won', hover_data=['boundaries_1', 'boundaries_2'])
fig.update_traces(marker_size=10)
#fig.update_layout(scattermode='group')
fig.show()
team_won 2 51 1 42 dtype: int64
print(wankhede_stadium.value_counts('team_won'))
fig = px.scatter(wankhede_stadium, x='runs_team1', y='runs_team2', color='team_won',
symbol='team_won', hover_data=['boundaries_1', 'boundaries_2'],
)
fig.update_traces(marker_size=10)
#fig.update_layout(scattermode='group')
fig.show()
team_won 2 64 1 53 3 1 dtype: int64
print(dubai_stadium.value_counts('team_won'))
fig = px.scatter(dubai_stadium, x='runs_team1', y='runs_team2', color='team_won',
hover_data=['boundaries_1', 'boundaries_2'],
color_discrete_sequence=['green', 'red','blue'])
fig.update_traces(marker_size=10)
#fig.update_layout(scattermode='group')
fig.show()
team_won 2 22 1 21 3 3 dtype: int64
def all_players_stats_year_list_team(player_list, all_data_list, player_df, year_list, team_list):
'''
This function takes in player_names list, all_game_data list,
player_df, year as text which is the dataframe containing all players data
and returns their stats
'''
for index, i in enumerate(player_list):
trophies = 0
strike_rate_30 = []
man_of_match = 0
multi_wickets = 0
matches = 0
innings_batted = 0
matches_won = 0
runs_scored = 0
balls_played = 0
catches = 0
run_outs = 0
innings_bowled = 0
balls_bowled = 0
runs_given = 0
wickets_taken = 0
four_scored = 0
six_scored = 0
four_given = 0
six_given = 0
extras_for = 0
extras_against = 0
scored_above_30 = 0
team_name = 0
for j in all_data_list:
scores_of_30 = 0
m_wickets = 0
balls_above_30 = 0
for year in year_list:
if j['info']['dates'][0][:4] == year:
for team in team_list:
try:
if i in j['info']['players'][team]:
#print(i)
team_name = 1
except:
None
values = list(j['info']['players'].values())
players_list = [x for xs in values for x in xs]
if i in players_list:
matches += 1
try:
if j['info']['player_of_match'][0] == i:
man_of_match += 1
except:
None
for k in j['innings']:
batsmen = []
bowler = []
for over in k['overs']:
for delivery in over['deliveries']:
if i == delivery['batter']:
runs_scored += delivery['runs']['batter']
scores_of_30 += delivery['runs']['batter']
if i not in batsmen:
batsmen.append(i)
balls_played += 1
balls_above_30 += 1
extras_for += delivery['runs']['extras']
if delivery['runs']['batter'] == 4:
four_scored += 1
elif delivery['runs']['batter'] == 6:
six_scored += 1
elif i == delivery['bowler']:
balls_bowled += 1
if i not in bowler:
bowler.append(i)
if delivery['runs']['batter'] == 4:
four_given += 1
elif delivery['runs']['batter'] == 6:
six_given += 1
runs_given += delivery['runs']['batter']
extras_against += delivery['runs']['extras']
try:
if delivery['wickets'][0]['kind'] != 'run out':
wickets_taken += 1
m_wickets += 1
elif delivery['wickets'][0]['fielders'][0]['name'] == i:
catches += 1
elif delivery['wickets'][0]['kind'] == 'run out':
run_outs += 1
except:
None
try:
if delivery['wickets'][0]['kind'] != 'run out':
if delivery['wickets'][0]['fielders'][0]['name'] == i:
catches += 1
if delivery['wickets'][0]['kind'] == 'run out':
if delivery['wickets'][0]['fielders'][0]['name'] == i:
run_outs += 1
except:
None
if i in batsmen:
innings_batted += 1
if i in bowler:
innings_bowled += 1
if scores_of_30 >= 30:
scored_above_30 += 1
strike_rate_30.append((scores_of_30/balls_above_30) * 100)
if m_wickets >= 2:
multi_wickets += 1
try:
if i in j['info']['players'][j['info']['outcome']['winner']]:
#print(j['info']['players'][j['info']['outcome']['winner']])
#print(i)
matches_won += 1
except:
continue
try:
if j['info']['event']['stage'] == 'Final':
if i in j['info']['players'][j['info']['outcome']['winner']]:
#print(j['info']['players'][j['info']['outcome']['winner']])
#print(i)
trophies += 1
except:
continue
try:
bat_avg = runs_scored/innings_batted
except:
bat_avg = 0.
try:
strike_rate = (runs_scored/balls_played) * 100
except:
strike_rate = 0.
try:
wickets_per_inni = wickets_taken/innings_bowled
except:
wickets_per_inni = 0.
try:
bowl_avg = runs_given/wickets_taken
except:
bowl_avg = 0.
try:
bowl_sr = balls_bowled/wickets_taken
except:
bowl_sr = 0.
try:
bowl_eco = runs_given/(balls_bowled/6)
except:
bowl_eco = 0.
strike_rate_above_30 = np.mean(strike_rate_30)
try:
formula_batter = (four_scored + six_scored + bat_avg) + (scored_above_30 *
strike_rate_above_30)
except:
formula_batter = 0.
try:
formula_bowler = (wickets_taken + multi_wickets) + (wickets_taken / innings_bowled)
except:
formula_bowler = 0.
try:
formula_fielder = man_of_match + catches + run_outs
except:
formula_fielder = 0.
player_df.loc[index,:] = [i, matches, innings_batted, innings_bowled, matches_won, runs_scored,
balls_played, bat_avg, strike_rate, catches, run_outs, balls_bowled,
runs_given, wickets_taken,
wickets_per_inni, bowl_avg, bowl_sr, bowl_eco, four_scored,
six_scored, four_given, six_given, extras_for, extras_against,
scored_above_30, strike_rate_above_30, multi_wickets, team_name, man_of_match,
trophies, formula_batter, formula_bowler, formula_fielder]
return player_df
all_cols = ['player_name', 'matches_played', 'innings_batted', 'innings_bowled', 'matches_won', 'runs_scored',
'balls_played', 'batting_avg', 'batting_strike_rate', 'catches', 'run_outs', 'balls_bowled', 'runs_given',
'wickets_taken', 'wickets_per_innings', 'bowling_avg', 'bowling_strike_rate', 'bowling_eco',
'four_scored', 'six_scored', 'four_given', 'six_given', 'extras_for', 'extras_against', 'scores_above_30',
'strike_rate_30', 'multi_wickets', 'team_y_n', 'man_of_match', 'trophies', 'formula_batter', 'formula_bowler', 'formula_fielder']
all_players_year_df = pd.DataFrame(columns=all_cols)
rcb_08 = all_players_stats_year_list_team(all_players, all_data, all_players_year_df, ['2008'], ['Royal Challengers Bangalore'])
all_players_year_df = pd.DataFrame(columns=all_cols)
rcb_09 = all_players_stats_year_list_team(all_players, all_data, all_players_year_df, ['2009'], ['Royal Challengers Bangalore'])
all_players_year_df = pd.DataFrame(columns=all_cols)
rcb_10 = all_players_stats_year_list_team(all_players, all_data, all_players_year_df, ['2010'], ['Royal Challengers Bangalore'])
all_players_year_df = pd.DataFrame(columns=all_cols)
rcb_11 = all_players_stats_year_list_team(all_players, all_data, all_players_year_df, ['2011'], ['Royal Challengers Bangalore'])
all_players_year_df = pd.DataFrame(columns=all_cols)
rcb_12 = all_players_stats_year_list_team(all_players, all_data, all_players_year_df, ['2012'], ['Royal Challengers Bangalore'])
all_players_year_df = pd.DataFrame(columns=all_cols)
rcb_13 = all_players_stats_year_list_team(all_players, all_data, all_players_year_df, ['2013'], ['Royal Challengers Bangalore'])
all_players_year_df = pd.DataFrame(columns=all_cols)
rcb_14 = all_players_stats_year_list_team(all_players, all_data, all_players_year_df, ['2014'], ['Royal Challengers Bangalore'])
all_players_year_df = pd.DataFrame(columns=all_cols)
rcb_15 = all_players_stats_year_list_team(all_players, all_data, all_players_year_df, ['2015'], ['Royal Challengers Bangalore'])
all_players_year_df = pd.DataFrame(columns=all_cols)
rcb_16 = all_players_stats_year_list_team(all_players, all_data, all_players_year_df, ['2016'], ['Royal Challengers Bangalore'])
all_players_year_df = pd.DataFrame(columns=all_cols)
rcb_17 = all_players_stats_year_list_team(all_players, all_data, all_players_year_df, ['2017'], ['Royal Challengers Bangalore'])
all_players_year_df = pd.DataFrame(columns=all_cols)
rcb_18 = all_players_stats_year_list_team(all_players, all_data, all_players_year_df, ['2018'], ['Royal Challengers Bangalore'])
all_players_year_df = pd.DataFrame(columns=all_cols)
rcb_19 = all_players_stats_year_list_team(all_players, all_data, all_players_year_df, ['2019'], ['Royal Challengers Bangalore'])
all_players_year_df = pd.DataFrame(columns=all_cols)
rcb_20 = all_players_stats_year_list_team(all_players, all_data, all_players_year_df, ['2020'], ['Royal Challengers Bangalore'])
all_players_year_df = pd.DataFrame(columns=all_cols)
rcb_21 = all_players_stats_year_list_team(all_players, all_data, all_players_year_df, ['2021'], ['Royal Challengers Bangalore'])
all_players_year_df = pd.DataFrame(columns=all_cols)
rcb_22 = all_players_stats_year_list_team(all_players, all_data, all_players_year_df, ['2022'], ['Royal Challengers Bangalore'])
all_players_year_df = pd.DataFrame(columns=all_cols)
rcb_23 = all_players_stats_year_list_team(all_players, all_data, all_players_year_df, ['2023'], ['Royal Challengers Bangalore'])
all_players_year_df = pd.DataFrame(columns=all_cols)
rcb_24 = all_players_stats_year_list_team(all_players, all_data, all_players_year_df, ['2024'], ['Royal Challengers Bangalore'])
formula_scatterplot(rcb_08, 'formula_batter', 'team_y_n',
'scores_above_30', 'runs_scored', 'strike_rate_30',
'six_scored', 'matches_played', ['player_name', 'innings_batted', 'batting_strike_rate', 'runs_scored', 'scores_above_30'])
all_players_year_df = pd.DataFrame(columns=all_cols)
rr_08 = all_players_stats_year_list_team(all_players, all_data, all_players_year_df, ['2008'], ['Rajasthan Royals'])
formula_scatterplot(rr_08, 'formula_batter', 'team_y_n',
'scores_above_30', 'runs_scored', 'strike_rate_30',
'six_scored', 'matches_played', ['player_name', 'innings_batted', 'batting_strike_rate', 'runs_scored', 'scores_above_30'])
formula_scatterplot(rcb_08, 'formula_bowler', 'team_y_n',
'bowling_eco', 'wickets_taken', 'six_given',
'multi_wickets', 'matches_played', ['player_name', 'innings_bowled', 'bowling_eco',
'wickets_taken'])
formula_scatterplot(rr_08, 'formula_bowler', 'team_y_n',
'bowling_eco', 'wickets_taken', 'six_given',
'multi_wickets', 'matches_played', ['player_name', 'innings_bowled', 'bowling_eco',
'wickets_taken'])
formula_scatterplot(rcb_09, 'formula_batter', 'team_y_n',
'scores_above_30', 'runs_scored', 'strike_rate_30',
'six_scored', 'matches_played', ['player_name', 'innings_batted', 'batting_strike_rate', 'runs_scored', 'scores_above_30'])
all_players_year_df = pd.DataFrame(columns=all_cols)
srh_09 = all_players_stats_year_list_team(all_players, all_data, all_players_year_df, ['2009'], ['Deccan Chargers'])
formula_scatterplot(srh_09, 'formula_batter', 'team_y_n',
'scores_above_30', 'runs_scored', 'strike_rate_30',
'six_scored', 'matches_played', ['player_name', 'innings_batted', 'batting_strike_rate', 'runs_scored', 'scores_above_30'])
formula_scatterplot(rcb_09, 'formula_bowler', 'team_y_n',
'bowling_eco', 'wickets_taken', 'six_given',
'multi_wickets', 'matches_played', ['player_name', 'innings_bowled', 'bowling_eco',
'wickets_taken'])
formula_scatterplot(srh_09, 'formula_bowler', 'team_y_n',
'bowling_eco', 'wickets_taken', 'six_given',
'multi_wickets', 'matches_played', ['player_name', 'innings_bowled', 'bowling_eco',
'wickets_taken'])
formula_scatterplot(rcb_10, 'formula_batter', 'team_y_n',
'scores_above_30', 'runs_scored', 'strike_rate_30',
'six_scored', 'matches_played', ['player_name', 'innings_batted', 'batting_strike_rate', 'runs_scored', 'scores_above_30'])
all_players_year_df = pd.DataFrame(columns=all_cols)
csk_10 = all_players_stats_year_list_team(all_players, all_data, all_players_year_df, ['2010'], ['Chennai Super Kings'])
formula_scatterplot(csk_10, 'formula_batter', 'team_y_n',
'scores_above_30', 'runs_scored', 'strike_rate_30',
'six_scored', 'matches_played', ['player_name', 'innings_batted', 'batting_strike_rate', 'runs_scored', 'scores_above_30'])
formula_scatterplot(rcb_10, 'formula_bowler', 'team_y_n',
'bowling_eco', 'wickets_taken', 'six_given',
'multi_wickets', 'matches_played', ['player_name', 'innings_bowled', 'bowling_eco',
'wickets_taken'])
formula_scatterplot(csk_10, 'formula_bowler', 'team_y_n',
'bowling_eco', 'wickets_taken', 'six_given',
'multi_wickets', 'matches_played', ['player_name', 'innings_bowled', 'bowling_eco',
'wickets_taken'])
formula_scatterplot(rcb_14, 'formula_batter', 'team_y_n',
'scores_above_30', 'runs_scored', 'strike_rate_30',
'six_scored', 'matches_played', ['player_name', 'innings_batted', 'batting_strike_rate', 'runs_scored', 'scores_above_30'])
all_players_year_df = pd.DataFrame(columns=all_cols)
kkr_14 = all_players_stats_year_list_team(all_players, all_data, all_players_year_df, ['2014'], ['Kolkata Knight Riders'])
formula_scatterplot(kkr_14, 'formula_batter', 'team_y_n',
'scores_above_30', 'runs_scored', 'strike_rate_30',
'six_scored', 'matches_played', ['player_name', 'innings_batted', 'batting_strike_rate', 'runs_scored', 'scores_above_30'])
formula_scatterplot(rcb_14, 'formula_bowler', 'team_y_n',
'bowling_eco', 'wickets_taken', 'six_given',
'multi_wickets', 'matches_played', ['player_name', 'innings_bowled', 'bowling_eco',
'wickets_taken'])
formula_scatterplot(kkr_14, 'formula_bowler', 'team_y_n',
'bowling_eco', 'wickets_taken', 'six_given',
'multi_wickets', 'matches_played', ['player_name', 'innings_bowled', 'bowling_eco',
'wickets_taken'])
formula_scatterplot(rcb_15, 'formula_batter', 'team_y_n',
'scores_above_30', 'runs_scored', 'strike_rate_30',
'six_scored', 'matches_played', ['player_name', 'innings_batted', 'batting_strike_rate', 'runs_scored', 'scores_above_30'])
all_players_year_df = pd.DataFrame(columns=all_cols)
mi_15 = all_players_stats_year_list_team(all_players, all_data, all_players_year_df, ['2015'], ['Mumbai Indians'])
formula_scatterplot(mi_15, 'formula_batter', 'team_y_n',
'scores_above_30', 'runs_scored', 'strike_rate_30',
'six_scored', 'matches_played', ['player_name', 'innings_batted', 'batting_strike_rate', 'runs_scored', 'scores_above_30'])
formula_scatterplot(rcb_15, 'formula_bowler', 'team_y_n',
'bowling_eco', 'wickets_taken', 'six_given',
'multi_wickets', 'matches_played', ['player_name', 'innings_bowled', 'bowling_eco',
'wickets_taken'])
formula_scatterplot(mi_15, 'formula_bowler', 'team_y_n',
'bowling_eco', 'wickets_taken', 'six_given',
'multi_wickets', 'matches_played', ['player_name', 'innings_bowled', 'bowling_eco',
'wickets_taken'])
formula_scatterplot(rcb_16, 'formula_batter', 'team_y_n',
'scores_above_30', 'runs_scored', 'strike_rate_30',
'six_scored', 'matches_played', ['player_name', 'innings_batted', 'batting_strike_rate', 'runs_scored', 'scores_above_30'])
all_players_year_df = pd.DataFrame(columns=all_cols)
srh_16 = all_players_stats_year_list_team(all_players, all_data, all_players_year_df, ['2016'], ['Sunrisers Hyderabad'])
formula_scatterplot(srh_16, 'formula_batter', 'team_y_n',
'scores_above_30', 'runs_scored', 'strike_rate_30',
'six_scored', 'matches_played', ['player_name', 'innings_batted', 'batting_strike_rate', 'runs_scored', 'scores_above_30'])
formula_scatterplot(rcb_16, 'formula_bowler', 'team_y_n',
'bowling_eco', 'wickets_taken', 'six_given',
'multi_wickets', 'matches_played', ['player_name', 'innings_bowled', 'bowling_eco',
'wickets_taken'])
formula_scatterplot(srh_16, 'formula_bowler', 'team_y_n',
'bowling_eco', 'wickets_taken', 'six_given',
'multi_wickets', 'matches_played', ['player_name', 'innings_bowled', 'bowling_eco',
'wickets_taken'])
formula_scatterplot(rcb_20, 'formula_batter', 'team_y_n',
'scores_above_30', 'runs_scored', 'strike_rate_30',
'six_scored', 'matches_played', ['player_name', 'innings_batted', 'batting_strike_rate', 'runs_scored', 'scores_above_30'])
all_players_year_df = pd.DataFrame(columns=all_cols)
mi_20 = all_players_stats_year_list_team(all_players, all_data, all_players_year_df, ['2020'], ['Mumbai Indians'])
formula_scatterplot(mi_20, 'formula_batter', 'team_y_n',
'scores_above_30', 'runs_scored', 'strike_rate_30',
'six_scored', 'matches_played', ['player_name', 'innings_batted', 'batting_strike_rate', 'runs_scored', 'scores_above_30'])
formula_scatterplot(rcb_20, 'formula_bowler', 'team_y_n',
'bowling_eco', 'wickets_taken', 'six_given',
'multi_wickets', 'matches_played', ['player_name', 'innings_bowled', 'bowling_eco',
'wickets_taken'])
formula_scatterplot(mi_20, 'formula_bowler', 'team_y_n',
'bowling_eco', 'wickets_taken', 'six_given',
'multi_wickets', 'matches_played', ['player_name', 'innings_bowled', 'bowling_eco',
'wickets_taken'])
formula_scatterplot(rcb_21, 'formula_batter', 'team_y_n',
'scores_above_30', 'runs_scored', 'strike_rate_30',
'six_scored', 'matches_played', ['player_name', 'innings_batted', 'batting_strike_rate', 'runs_scored', 'scores_above_30'])
all_players_year_df = pd.DataFrame(columns=all_cols)
csk_21 = all_players_stats_year_list_team(all_players, all_data, all_players_year_df, ['2021'], ['Chennai Super Kings'])
formula_scatterplot(csk_21, 'formula_batter', 'team_y_n',
'scores_above_30', 'runs_scored', 'strike_rate_30',
'six_scored', 'matches_played', ['player_name', 'innings_batted', 'batting_strike_rate', 'runs_scored', 'scores_above_30'])
formula_scatterplot(rcb_21, 'formula_bowler', 'team_y_n',
'bowling_eco', 'wickets_taken', 'six_given',
'multi_wickets', 'matches_played', ['player_name', 'innings_bowled', 'bowling_eco',
'wickets_taken'])
formula_scatterplot(csk_21, 'formula_bowler', 'team_y_n',
'bowling_eco', 'wickets_taken', 'six_given',
'multi_wickets', 'matches_played', ['player_name', 'innings_bowled', 'bowling_eco',
'wickets_taken'])
formula_scatterplot(rcb_22, 'formula_batter', 'team_y_n',
'scores_above_30', 'runs_scored', 'strike_rate_30',
'six_scored', 'matches_played', ['player_name', 'innings_batted', 'batting_strike_rate', 'runs_scored', 'scores_above_30'])
all_players_year_df = pd.DataFrame(columns=all_cols)
gt_22 = all_players_stats_year_list_team(all_players, all_data, all_players_year_df, ['2022'], ['Gujarat Titans'])
formula_scatterplot(gt_22, 'formula_batter', 'team_y_n',
'scores_above_30', 'runs_scored', 'strike_rate_30',
'six_scored', 'matches_played', ['player_name', 'innings_batted', 'batting_strike_rate', 'runs_scored', 'scores_above_30'])
formula_scatterplot(rcb_22, 'formula_bowler', 'team_y_n',
'bowling_eco', 'wickets_taken', 'six_given',
'multi_wickets', 'matches_played', ['player_name', 'innings_bowled', 'bowling_eco',
'wickets_taken'])
formula_scatterplot(gt_22, 'formula_bowler', 'team_y_n',
'bowling_eco', 'wickets_taken', 'six_given',
'multi_wickets', 'matches_played', ['player_name', 'innings_bowled', 'bowling_eco',
'wickets_taken'])